System and method for online information, employment, social and other compatibility search, matching and ranking

ABSTRACT

A computer server system and method are disclosed for personalization and customizable filtering of network search results and search result rankings, such as for Internet searching. A representative server system comprises: a network interface to receive a query from a respondent or co-respondent; at least one data storage device storing a plurality of return queries; and one or more processors adapted to access the data storage device and using the query, to select the return queries for transmission; to search the data storage device for corresponding pluralities of responses to the return queries from other co-respondents or respondents; to pair-wise score the responses and generate pair-wise alignment scores for respondent and co-respondent combinations; to sort and rank the combinations according to the alignment scores; and to output a listing of the sorted and ranked respondents or co-respondents to form the personalized network search results and search result rankings.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to and thebenefit of U.S. patent application Ser. No. 16/679,792, filed Nov. 11,2019, inventors Brian Balasia et al., titled “System and Method ForOnline Information, Employment, Social and Other Compatibility Search,Matching and Ranking”, which is a continuation of and claims priority toand the benefit of U.S. patent application Ser. No. 16/403,509, filedMay 4, 2019 and issued Dec. 17, 2019 as U.S. Pat. No. 10,510,045,inventors Brian Balasia et al., titled “System and Method For OnlineInformation, Employment, Social and Other Compatibility Search, Matchingand Ranking”, which is a continuation of and claims priority to and thebenefit of U.S. patent application Ser. No. 15/411,984, filed Jan. 21,2017 and issued May 21, 2019 as U.S. Pat. No. 10,296,873, inventorsBrian Balasia et al., titled “System and Method For Online Information,Employment, Social and Other Compatibility Search, Matching andRanking”, which is a nonprovisional of and claims priority to and thebenefit of U.S. Provisional Patent Application No. 62/286,349, filedJan. 23, 2016, inventors Brian Balasia et al., titled “System and MethodFor Online Information, Employment, Social and Other CompatibilitySearch, Matching and Ranking”, which are commonly assigned herewith,incorporated herein by reference with the same full force and effect asif set forth in their entireties herein, and with priority claimed forall commonly disclosed subject matter.

FIELD OF THE INVENTION

The present invention relates generally to online search engines, andmore specifically to personalization of Internet-based information,employment, social, and other compatibility searching, matching andranking.

BACKGROUND OF THE INVENTION

The current state of the art in online information, employment, social,and other compatibility searching, matching and ranking is highlylimited. While online search engines (generally using word-based orphrase-based searching) are reasonably advanced in their ability toretrieve documents (e.g., web pages, images, files, etc.), that areresponsive to the terms of a query, typically searched and retrievedusing keywords contained in the query, these keyword search methods donot provide high quality results for employment opportunities or otherinformation, employment, social, and other compatibility search,matching and ranking. While such search engines typically return resultsthat accurately correspond to the search terms (keywords) of the query,the search results may not reflect the user's underlying interests andgoals, both of which are highly significant for finding suitable, highlycompatible employment opportunities or social engagements, for example.

Additionally, using such keyword searching, too many search results andan insufficient quality of the search results from may be returned bythe search engine, creating several problems. First, so many results arereturned that the user cannot review all of the results in a reasonableperiod of time, or within the time allocated for review by the user.Second, because of the large number of results, search providerstypically return results which have been ranked according to somecriteria applied by the search provider, such as by the Google pageranking system based upon the number of links to a selected web page (orwebsite) provided by third parties as an indicator of the importance ofthe selected web page (or website).

In many cases, the ranked search results are distorted, both by beingover-inclusive in the results returned, and by distortion of therankings of the results. For example, keyword searching can be “gamed”,with websites or documents including various keywords simply to beincluded in ranked results, resulting in over-inclusion of theseotherwise irrelevant websites or documents in the search results. Alsofor example, keywords can be purchased from a search provider, oftenthrough a daily bidding process, resulting in distorted search resultrankings, with the highest rankings in the search results going to thehighest bidder.

Not only does this result in overall inaccuracy of the results returned,but also it increases the amount of data which must be transmitted tothe user, much of which is irrelevant and which serves to obscure orbury the relevant data sought by the user of the search engine,essentially hiding the relevant “needle” in the irrelevant “haystack”.The increased amount of transmitted data also tends to require largerdatabases for data storage, larger system server and memoryrequirements, and further serves to overload various network andInternet systems and effectively increase the overall search time.

In addition, this type of Internet searching may also beunder-inclusive, missing the most relevant information which may notutilize the particular keyword and failing to return relevant results.

These problems of over-inclusiveness, under-inclusiveness and distortedrankings creates additional problems in many industries, particularlyemployment, and are further compounded by aggregator websites. Forexample, many employment search or job posting websites simply aggregatejob postings and job advertisements from many other sites and locations,and do not provide any mechanism to find or locate the most relevantemployment opportunities or candidates. For example, in employmentsearching, resumes are often created to using typical search keywords,so that an applicant's name and resume will be in the search resultsreturned in a keyword search by a potential employer. The end resultsare that an individual's resume is submitted to hundreds of companies,with the corollary result that a company may receive thousands tohundreds of thousands of resumes for job postings which cannot beeffectively winnowed or reduced through additional keyword searching,and again means that the recruiter (such as a potential employer) cannotreview all of the resume results in a reasonable period of time, orwithin the time allocated for review by the employer (e.g., the timeinterval between receipt of the search results and when the applicantwould be expected to interview and start employment). For example, somany resumes may be received which would require hundreds ofperson-hours to review, while only several (e.g., 2-3) person-hours maybe allocated to review the submitted resumes, making a thorough revieweffectively impossible.

As a further result, search results returned in these over-inclusivesituations do not provide fully actionable information. For example,when faced with a thousand resumes for a job posting, a potentialemployer may simply pick several which are literally at the top of thestack, such as a stack of resumes ordered based on the time each wasreceived (if at all), or may pick a candidate based on an uneducatedreferral (such as from a relative, friend, or coworker), potentiallyoverlooking many more qualified candidates. In addition, the end resultfor a job applicant may be multiple and undesired inquiries frompotential employers offering jobs for which the applicant has nointerest. These poor search results have associated costs, both in thetime and effort spent searching, and in employee turnover.

A need remains, therefore, for a system and method for personalizationof Internet-based employment search results and employment search resultranking, such as for online employment compatibility search, matchingand ranking, using a search engine. A need also remains for a system andmethod for customizable filtering of Internet-based search results andsearch result ranking in a search engine. Such a search engine systemand method should provide an alternative to keyword searching, andshould produce actionable results, such as returning a reasonable numberof employment search results of high quality, that are directly relevantto the personalized search and without being under-inclusive, andfurther which can be reviewed by the user within the user's timeallocation. Such a search engine should also result in a decrease in theamount of data required to be stored and decrease the corresponding sizeof the resulting databases, further serving to decrease the amount ofdata required to be transmitted and reduce the system load. In addition,such a search engine system and method should incorporate timesensitivity in the personalized search results and provide correspondinguser notifications.

SUMMARY OF THE INVENTION

The representative or exemplary embodiments of the present inventionprovide numerous advantages. Representative embodiments provide for atechnical, artificial intelligence solution to an Internet-centricproblem of over-inclusiveness of search results, under-inclusiveness ofrelevant information, and distorted rankings of search results using theprior art keyword searching. The representative embodiments automate theInternet-based searching and selection processes using highly relevant,user-determined characteristics and user-customizable parameters,resulting in personalization of search results and search resultranking. The representative embodiments further automate theInternet-based searching and selection processes using highly relevant,user-determined and centrally-located filters, also resulting inpersonalization of search results and search result ranking. Therepresentative embodiments employ artificial intelligence to “match”information to a user (as a respondent or co-respondent) and provideexactly the information the user wants or needs (if available) at thepoint in time wanted or needed, without inundating the user withthousands of responses or documents which the user cannot possiblyreview in a reasonable or allocated time, and without beingunder-inclusive of highly relevant search results.

As a result, the representative embodiments improve the functioning ofInternet-based searches, providing highly personalized search resultsand search result rankings, thereby dramatically decreasing the amountof search time required for a user to discover relevant and actionableinformation.

As a further result, the representative embodiments improve thefunctioning of Internet-based searches, decreasing the amount of datawhich must be transmitted to provide the highly personalized searchresults and search result rankings, decreasing the size of the databasesrequired for data storage, decreasing the system server and memoryrequirements, and further serving to decrease the load of the varioussystem components, such as the Internet-based servers and routers.

A representative embodiment provides computer server system coupleableto a network for personalization of network search results and searchresult rankings. A representative server system comprises: a networkinput and output interface for network data transmission and reception,the network input and output interface adapted to receive at least onequery from a respondent or co-respondent via the network; to transmit aplurality of return queries to the respondent or co-respondent via thenetwork; to receive a plurality of responses to the return queries fromthe respondent or co-respondent via the network; and to transmitpersonalized network search results and search result rankings to therespondent or co-respondent via the network; at least one data storagedevice storing a plurality of return queries; and one or more processorscoupled to the at least one data storage device and network input andoutput interface, the one or more processors adapted to access the atleast one data storage device and using the at least one query, toselect the plurality of return queries for transmission; to search theat least one data storage device for corresponding pluralities ofresponses to the return queries from one or more co-respondents orrespondents, respectively; to comparatively pair-wise score theplurality of responses to the return queries against the correspondingpluralities of responses to the return queries and generate a pluralityof pair-wise alignment scores for a plurality of respondent andco-respondent combinations; to sort and rank the plurality of respondentand co-respondent combinations according to the plurality of pair-wisealignment scores; and to output a listing of the sorted and rankedrespondents or co-respondents to form the personalized network searchresults and search result rankings.

In a representative embodiment, the one or more processors are furtheradapted to select one or more co-respondents or respondents from thesorted and ranked plurality of respondent and co-respondent combinationsfor inclusion of a predetermined number of sorted and ranked respondentsor co-respondents to form the personalized network search results andsearch result rankings.

In a representative embodiment, the one or more processors may befurther adapted, for each response of the plurality of responses to thereturn queries, to determine an unmodified distance between responses ofa respondent and a co-respondent; and to combine a plurality ofunmodified distance determinations for the plurality of responses to thereturn queries to form an unmodified alignment score. In such arepresentative embodiment, the one or more processors may be furtheradapted, for each response of the plurality of responses to the returnqueries, to determine a normalized distance between responses of arespondent and a co-respondent; and to combine a plurality of normalizeddistance determinations for the plurality of responses to the returnqueries to form a normalized alignment score. Also in such arepresentative embodiment, the one or more processors may be furtheradapted to differentially weight the unmodified alignment score andnormalized alignment score; and to combine the differentially weightedunmodified alignment score and normalized alignment score to form thepair-wise alignment score.

In a representative embodiment, the one or more processors may befurther adapted to generate a digital filter from each plurality ofresponses to the return queries to form a plurality of digital filters.For example, each digital filter of the plurality of digital filters maycomprise a matrix or vector having the pluralities of responses to thereturn queries for a selected respondent or co-respondent.

In a representative embodiment, the one or more processors may befurther adapted to compare a selected combination of respondent andco-respondent digital filters, of the plurality of digital filters, togenerate the pair-wise alignment score for the selected respondent andco-respondent combination. For example, the comparison may be a variancedetermination or a difference determination.

In a representative embodiment, the one or more processors may befurther adapted to use the received query as an index into the at leastone data storage device.

In another representative embodiment, the one or more processors may befurther adapted to store the plurality of pair-wise alignment scores forthe plurality of respondent and co-respondent combinations in the atleast one data storage device. In a representative embodiment, the oneor more processors are further adapted to store the listing of thesorted and ranked respondents or co-respondents in the at least one datastorage device.

In another representative embodiment, the one or more processors may befurther adapted to generate a push notification of the personalizednetwork search results and search result rankings for transmission bythe network input and output interface to the respondent orco-respondent.

In a representative embodiment, the one or more processors may befurther adapted to additionally filter the listing of the sorted andranked respondents or co-respondents using a user-selectable parameterof a plurality of user-selectable parameters. For example, the pluralityof user-selectable parameters may comprise at least one parameterselected from the group consisting of: previous employer, currentemployer, previous employee, current employee, citizenship, disabilitystatus, visa status, and military service.

In a representative embodiment, the respondent may be an employmentcandidate and the co-respondent may be a potential employer, and whereinthe listing of the sorted and ranked respondents or co-respondentscomprises a listing of sorted and ranked employment candidates providedto the potential employer or comprises a listing of sorted and rankedpotential employers provided to the employment candidate.

In a representative embodiment, each return query a first plurality ofreturn queries to the respondent is a corollary to each return query ofa second plurality of return queries to the co-respondent. In anotherrepresentative embodiment, each return query of the plurality of returnqueries may pertain to a characteristic of the at least one query. Forexample, each return query of the plurality of return queries to arespondent may pertain to a preference or interest level of one or morecharacteristics of the at least one query. Also for example, each returnquery of the plurality of return queries to a co-respondent may pertainto an expected amount of time for engaging in one or more activitiesrelated to the at least one query.

In another representative embodiment, the computer server system mayfurther comprise: a client device coupled to the network for selectionof the at least one query from a drop down menu provided on a graphicaluser interface.

In a representative embodiment, the at least one query may be anemployment position. In another representative embodiment, the at leastone query may be a social matching request.

A computer server-implemented method for personalization of networksearch results and search result rankings is also disclosed. Arepresentative method may comprise: using the computer server, receivingat least one query from a respondent or co-respondent via the network;in response to the at least one query, using the computer server,accessing at least one data storage device and selecting a plurality ofreturn queries; using the computer server, transmitting the plurality ofreturn queries to the respondent or co-respondent via the network; usingthe computer server, receiving a plurality of responses to the returnqueries from the respondent or co-respondent via the network; using thecomputer server, searching the at least one data storage device forcorresponding pluralities of responses to the return queries from one ormore co-respondents or respondents, respectively; using the computerserver, comparatively pair-wise scoring the plurality of responses tothe return queries against the corresponding pluralities of responses tothe return queries and generating a plurality of pair-wise alignmentscores for a plurality of respondent and co-respondent combinations;using the computer server, sorting and ranking the plurality ofrespondent and co-respondent combinations according to the plurality ofpair-wise alignment scores; and using the computer server, outputting alisting of the sorted and ranked respondents or co-respondents to formthe personalized network search results and search result rankings.

In a representative embodiment, the computer server-implemented methodmay further comprise: using the computer server, selecting one or moreco-respondents or respondents from the sorted and ranked plurality ofrespondent and co-respondent combinations for inclusion of apredetermined number of sorted and ranked respondents or co-respondentsto form the personalized network search results and search resultrankings.

In a representative embodiment, the pair-wise scoring may furthercomprise: for each response of the plurality of responses to the returnqueries, using the computer server, determining an unmodified distancebetween responses of a respondent and a co-respondent; and using thecomputer server, combining a plurality of unmodified distancedeterminations for the plurality of responses to the return queries toform an unmodified alignment score. In such a representative embodiment,the pair-wise scoring may further comprise: for each response of theplurality of responses to the return queries, using the computer server,determining a normalized distance between responses of a respondent anda co-respondent; and using the computer server, combining a plurality ofnormalized distance determinations for the plurality of responses to thereturn queries to form a normalized alignment score. Also in such arepresentative embodiment, the pair-wise scoring may further comprise:using the computer server, differentially weighting the unmodifiedalignment score and normalized alignment score; and using the computerserver, combining the differentially weighted unmodified alignment scoreand normalized alignment score to form the pair-wise alignment score.

In a representative embodiment, the computer server-implemented methodmay further comprise: using the computer server, generating a digitalfilter from each plurality of responses to the return queries to form aplurality of digital filters. For example, each digital filter of theplurality of digital filters may comprise a matrix or vector having thepluralities of responses to the return queries for a selected respondentor co-respondent.

In a representative embodiment, the computer server-implemented methodmay further comprise: using the computer server, comparing a selectedcombination of respondent and co-respondent digital filters, of theplurality of digital filters, to generate the pair-wise alignment scorefor the selected respondent and co-respondent combination. For example,in a representative embodiment, the comparison may be a variancedetermination or a difference determination.

In a representative embodiment, the one or more processors may befurther adapted to use the plurality of digital filters to provide atwo-stage filtering of potential search results through both arespondent digital filter of a selected respondent and a co-respondentdigital filter of a selected co-respondent, of the plurality of digitalfilters, to generate the personalized network search results and searchresult rankings for the selected respondent or the selectedco-respondent.

In a representative embodiment, the selection of the plurality of returnqueries may further comprise: using the computer server, using thereceived query as an index into the at least one data storage device.

In a representative embodiment, the computer server-implemented methodmay further comprise: using the computer server, storing the pluralityof pair-wise alignment scores for the plurality of respondent andco-respondent combinations in the at least one data storage device. In arepresentative embodiment, the computer server-implemented method mayfurther comprise: using the computer server, storing the listing of thesorted and ranked respondents or co-respondents in the at least one datastorage device.

In another representative embodiment, the computer server-implementedmethod may further comprise: using the computer server, generating andtransmitting a push notification of the personalized network searchresults and search result rankings to the respondent or co-respondent.

In yet another representative embodiment, the computerserver-implemented method may further comprise: using the computerserver, filtering the listing of the sorted and ranked respondents orco-respondents using a user-selectable parameter of a plurality ofuser-selectable parameters. For example, the plurality ofuser-selectable parameters comprises at least one parameter selectedfrom the group consisting of: previous employer, current employer,previous employee, current employee, citizenship, disability status,visa status, and military service.

In a representative embodiment, the computer server-implemented methodmay further comprise: selecting the at least one query from a drop downmenu provided on a graphical user interface.

A representative embodiment provides computer server system coupleableto a network for personalization of network search results and searchresult rankings. A representative server system comprises: a networkinput and output interface for network data transmission and reception,the network input and output interface adapted to receive at least onequery from a respondent or co-respondent via the network; to transmit aplurality of return queries to the respondent or co-respondent via thenetwork; to receive a plurality of responses to the return queries fromthe respondent or co-respondent via the network; to transmitpersonalized network search results and search result rankings to therespondent or co-respondent via the network; and to transmit a pushnotification of the personalized network search results and searchresult rankings to the respondent or co-respondent via the network; atleast one data storage device storing a plurality of return queries; andone or more processors coupled to the at least one data storage deviceand network input and output interface, the one or more processorsadapted to access the at least one data storage device and using the atleast one query, to select the plurality of return queries fortransmission; to search the at least one data storage device forcorresponding pluralities of responses to the return queries from one ormore co-respondents or respondents, respectively; to comparativelypair-wise score the plurality of responses to the return queries againstthe corresponding pluralities of responses to the return queries usingdifferentially weighted unmodified alignment scores and normalizedalignment scores and generate a plurality of pair-wise alignment scoresfor a plurality of respondent and co-respondent combinations; to sortand rank the plurality of respondent and co-respondent combinationsaccording to the plurality of pair-wise alignment scores; to output alisting of the sorted and ranked respondents or co-respondents to formthe personalized network search results and search result rankings; andto generate the push notification of the personalized network searchresults and search result rankings to the respondent or co-respondent.

Another representative embodiment provides computer server systemcoupleable to a network for personalization of network search resultsand search result rankings. A representative server system comprises: anetwork input and output interface for network data transmission andreception, the network input and output interface adapted to receive atleast one query from an employment candidate as a respondent or apotential employer as a co-respondent via the network, the at least onequery pertaining to an employment position; to transmit a plurality ofreturn queries to the respondent or co-respondent via the network; toreceive a plurality of responses to the return queries from therespondent or co-respondent via the network; and to transmitpersonalized network employment search results and search resultrankings to the respondent or co-respondent via the network; at leastone data storage device storing a plurality of return queries; and oneor more processors coupled to the at least one data storage device andnetwork input and output interface, the one or more processors adaptedto access the at least one data storage device and using the at leastone query, to select the plurality of return queries pertaining to acharacteristic of the employment position for transmission; to searchthe at least one data storage device for corresponding pluralities ofresponses to the return queries from one or more co-respondents orrespondents, respectively; to comparatively pair-wise score theplurality of responses to the return queries against the correspondingpluralities of responses to the return queries and generate a pluralityof pair-wise alignment scores for a plurality of respondent andco-respondent combinations; to sort and rank the plurality of respondentand co-respondent combinations according to the plurality of pair-wisealignment scores; and to output a listing of the sorted and rankedrespondents or co-respondents to form the personalized networkemployment search results and search result rankings.

A computer server-implemented method for personalization ofInternet-based employment search results and search result rankings isalso disclosed. A representative method comprises: using the computerserver, receiving at least one query from an employment candidate asrespondent or a potential employer as a co-respondent via the network,the at least one query pertaining to an employment position; in responseto the at least one query, using the computer server, accessing at leastone data storage device and selecting a plurality of return queriespertaining to a characteristic of the employment position; using thecomputer server, transmitting the plurality of return queries to therespondent or co-respondent via the network; using the computer server,receiving a plurality of responses to the return queries from therespondent or co-respondent via the network; using the computer server,searching the at least one data storage device for correspondingpluralities of responses to the return queries from one or moreco-respondents or respondents, respectively; using the computer server,comparatively pair-wise scoring the plurality of responses to the returnqueries against the corresponding pluralities of responses to the returnqueries and generating a plurality of pair-wise alignment scores for aplurality of respondent and co-respondent combinations; using thecomputer server, sorting and ranking the plurality of respondent andco-respondent combinations according to the plurality of pair-wisealignment scores; using the computer server, selecting one or moreco-respondents or respondents from the sorted and ranked plurality ofrespondent and co-respondent combinations; and using the computerserver, outputting a listing of the sorted and ranked respondents orco-respondents to form the personalized network employment searchresults and search result rankings.

In a representative embodiment, the listing of the sorted and rankedrespondents or co-respondents may comprise a listing of sorted andranked employment candidates provided to the potential employer orcomprises a listing of sorted and ranked potential employers provided tothe employment candidate.

Another representative embodiment provides computer server systemcoupleable to a network for personalization of network search resultsand search result rankings. A representative server system comprises: anetwork input and output interface for network data transmission andreception, the network input and output interface adapted to receive atleast one query from an employment candidate as a respondent or apotential employer as a co-respondent via the network, the at least onequery pertaining to an employment position; to transmit a plurality ofreturn queries to the respondent or co-respondent via the network; toreceive a plurality of responses to the return queries from therespondent or co-respondent via the network; to transmit personalizednetwork employment search results and search result rankings to therespondent or co-respondent via the network; and to transmit a pushnotification of the personalized network employment search results andsearch result rankings to the respondent or co-respondent via thenetwork; at least one data storage device storing a plurality of returnqueries; and one or more processors coupled to the at least one datastorage device and network input and output interface, the one or moreprocessors adapted to access the at least one data storage device andusing the at least one query, to select the plurality of return queriespertaining to a characteristic of the employment position fortransmission; to search the at least one data storage device forcorresponding pluralities of responses to the return queries from one ormore co-respondents or respondents, respectively; to comparativelypair-wise score the plurality of responses to the return queries againstthe corresponding pluralities of responses to the return queries usingdifferentially weighted unmodified alignment scores and normalizedalignment scores and generate a plurality of pair-wise alignment scoresfor a plurality of respondent and co-respondent combinations; to sortand rank the plurality of respondent and co-respondent combinationsaccording to the plurality of pair-wise alignment scores; to output alisting of the sorted and ranked respondents or co-respondents to formthe personalized network employment search results and search resultrankings; and to generate the push notification of the personalizednetwork employment search results and search result rankings to therespondent or co-respondent.

Numerous other advantages and features of the present invention willbecome readily apparent from the following detailed description of theinvention and the embodiments thereof, from the claims and from theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, features and advantages of the present invention will bemore readily appreciated upon reference to the following disclosure whenconsidered in conjunction with the accompanying drawings, wherein likereference numerals are used to identify identical components in thevarious views, and wherein reference numerals with alphabetic charactersare utilized to identify additional types, instantiations or variationsof a selected component embodiment in the various views, in which:

FIG. 1 is a block diagram illustrating an exemplary or representativesystem embodiment.

FIG. 2 is a block diagram illustrating an exemplary or representativeserver system or apparatus embodiment.

FIG. 3 is a block diagram illustrating an exemplary or representativeclient device embodiment.

FIGS. 4A and 4B (collectively referred to as FIG. 4 ) is a flow diagramillustrating an exemplary or representative method embodiment forpersonalization of search results and search result ranking in a searchengine.

FIGS. 5A and 5B (collectively referred to as FIG. 5 ) is a flow diagramillustrating an exemplary or representative method embodiment forpair-wise alignment score determination for personalization of searchresults and search result ranking in a search engine.

FIG. 6 is a block diagram illustrating exemplary or representativemessage transmission sequences for personalization of search results andsearch result ranking in a search engine.

FIG. 7 is a diagram illustrating an exemplary or representativegraphical user interface (“GUI”) providing user-selectable initialclient-side queries.

FIGS. 8A and 8B (collectively referred to as FIG. 8 ) are pairedrespondent and co-respondent diagrams illustrating exemplary orrepresentative graphical user interfaces illustrating representativereturn queries providing user-selectable parameters for pair-wise scoredetermination for personalization of search results and search resultranking in a search engine.

FIGS. 9A and 9B (collectively referred to as FIG. 9 ) are sequences ofdiagrams illustrating exemplary or representative graphical userinterfaces, respectively, for output of personalized search results andsearch result ranking, and for follow on communications.

FIG. 10 is a diagram illustrating exemplary or representative responsesof respondents and co-respondents to return queries for pair-wise scoredetermination for personalization of search results and search resultranking in a search engine.

FIGS. 11A, 11B, 11C, 11D and 11E (collectively referred to as FIG. 11 )are diagrams illustrating respondent and co-respondent customizeddigital filters, represented both as matrices (FIGS. 11A, 11B) andvectors (FIG. 11C), and with corresponding difference vectors (FIGS. 11Dand 11E).

FIG. 12 is a flow diagram illustrating an exemplary or representativemethod embodiment for pair-wise alignment score determination usingcustomized digital filters for customized filtering and/orpersonalization of search results and search result ranking in a searchengine.

DETAILED DESCRIPTION OF REPRESENTATIVE EMBODIMENTS

While the present invention is susceptible of embodiment in manydifferent forms, there are shown in the drawings and will be describedherein in detail specific exemplary embodiments thereof, with theunderstanding that the present disclosure is to be considered as anexemplification of the principles of the invention and is not intendedto limit the invention to the specific embodiments illustrated. In thisrespect, before explaining at least one embodiment consistent with thepresent invention in detail, it is to be understood that the inventionis not limited in its application to the details of construction and tothe arrangements of components set forth above and below, illustrated inthe drawings, or as described in the examples. Methods and apparatusesconsistent with the present invention are capable of other embodimentsand of being practiced and carried out in various ways. Also, it is tobe understood that the phraseology and terminology employed herein, aswell as the abstract included below, are for the purposes of descriptionand should not be regarded as limiting.

As described in greater detail below, the representative embodimentsprovide a technical, artificial intelligence solution to anInternet-centric problem of over-inclusiveness of search results anddistorted rankings of search results using the prior art keywordsearching. Just as a computing system uses sensor information andreverse-Bayesian computations to enable driving an automobile withouthuman control, the representative embodiments automate theInternet-based searching and selection processes using highly relevant,user-determined characteristics and user-customizable parameters,resulting in personalization of search results, customized filtering andsearch result ranking. Stated another way, the representativeembodiments employ artificial intelligence, search personalization, andcustomized search filtering to “match” information to a user (as arespondent or co-respondent) and provide the relevant and rankedinformation the user wants or needs (if available) at the point in timewanted or needed, without inundating the user with thousands ofresponses or documents which the user cannot possibly review in areasonable or allocated time.

In addition, the user-determined characteristics, user-customizableparameters, and customized filtering are stored in one or more memorystorage devices of the representative embodiments and persist over apredetermined period of time, such as several months. As a result, theuser-determined characteristics, user-customizable parameters, andcustomized filtering can be utilized repeatedly and periodically (e.g.,every time a user logs in to the representative system embodimentsand/or when searches are run periodically or at regular intervals by therepresentative system embodiments).

The personalized search results and search result rankings can then be“pushed” to the user at these periodic or regular intervals, or when theuser has been included in personalized network search results and searchresult rankings for another respondent or co-respondent, in addition towhen requested by the user, providing corresponding user notificationswhich are especially significant for time-sensitive information. Forexample, a push notification of a potential employment opportunity maybe sent via SMS or text to a user's smartphone or other device, causinga messaging application to open on the smartphone or other device, anddisplaying an Internet link for the user to access for learning greaterdetails about the information provided in the push notification. Alsofor example, a push notification of a potential employment opportunitymay be sent via any applicable communication method to a user'ssmartphone or other device, triggering or otherwise causing a dedicatedapplication to open on the smartphone or other device, which thendisplays the details about the information provided in the pushnotification, and further which provides various mechanisms for the userto “lock” or “pin” the information, such as using a Request to Connectbutton 955 provided on a GUI 950 of the dedicated application, asdescribed in greater detail below, allowing an effectively immediateuser response to highly time-sensitive information of the pushnotification.

Another representative embodiment provides a highly new and noveltwo-stage filtering to generate personalized network search results andsearch result rankings for a user. One (first) stage of the filtering isuser-customizable, based upon the (first) user's responses to returnqueries 615, 620, as discussed in greater detail below. The second stageof filtering, however, is customizable by a third party, namely,another, second user, as a respondent or co-respondent, whoseinformation may or may not be returned to the (first) user in the firstuser's personalized network search results and search result rankings,depending upon the alignment between the first user's digital filter andthe third party, second user's digital filter.

As described in greater detail below, the representative embodimentsimprove the functioning of Internet-based searches, providing highlypersonalized search results, search filtering, and search resultrankings, thereby dramatically decreasing the amount of search timerequired for a user to discover relevant and actionable information.Such representative embodiments also result in a decrease in the amountof data required to be stored and decrease the corresponding size of theresulting databases, further serving to decrease the amount of datarequired to be transmitted and reduce the system load. In addition,representative embodiments incorporate time sensitivity in thepersonalized search results and provide corresponding usernotifications.

FIG. 1 is a block diagram illustrating an exemplary or representativesearch system 100 for personalization of search results and searchresult ranking in a search engine. FIG. 2 is a block diagramillustrating an exemplary or representative (Internet-based or “cloud”based) server system (equivalently referred to as a computer server) orapparatus 200 for personalization of search results and search resultranking in a search engine, typically utilized in the search system 100.FIG. 3 is a block diagram illustrating an exemplary or representativeclient device 300. The system 100 is an example of an informationretrieval system in which the systems, components and techniquesdescribed below can be implemented. Although several components areillustrated, there may be fewer or more components in the system 100.Moreover, the components can be distributed on one or more computingdevices connected by one or more networks or other suitablecommunication mediums.

Referring to FIGS. 1-3 , as illustrated, the exemplary search system 100comprises at least one computer server system or apparatus 200 coupledthrough a network 110 (such as the Internet) (along with other networkequipment and various components such as a router 115, a wireless router120, a switching center 125 and/or base station 130) to a plurality ofclient devices 300, 300A. A user can interact with the search system 100through one or more client devices 300, 300A. Representative clientdevices 300, 300A include, for example and without limitation, acomputer, a supercomputer, a personal computer, an engineeringworkstation, a mainframe computer, a tablet computing device, a mobiletelephone or smartphone, or any other type of data processing device.For example, the client device 300, 300A can be a computer terminalwithin a local area network (LAN) or wide area network (WAN).

Continuing to refer to FIG. 1 , as illustrated, the exemplary network110 may be of any type of kind, using any medium such as wired, optical,or wireless, using any current or future protocols, such as InternetProtocol (“IP”), Transmission Control Protocol (“TCP”) (collectivelywith IP referred to as “TCP/IP”), which may further incorporate othercurrent or future protocols, such as hypertext transfer protocol(“HTTP”), various email and file transfer protocols (e.g., SMTP, FTP),or other types of networks, such as the public switched telephonenetwork (“PSTN”), cellular, LTE, GSM, EDGE, GPRS, Institute ofElectrical and Electronic Engineers (“IEEE”) 802.11, CDMA, WCDMA, or 3G,or any other network which provides for communication for data, voice ormultimedia, for user input, selection, evaluation, reporting, mediaprovision, and so on. The network 110, in turn, may be utilized toprovide any type of communication between and among the at least onecomputer server system or apparatus 200, the client devices 300, 300A,and any of the other illustrated devices, and may be directly orindirectly coupled to any of a plurality of such devices for suchInternet, voice, multimedia or any other form of data communication,whether switched or routed, including without limitation router(s) 115,wireless router(s) 120, and server(s) 200 of any type or kind (and whichmay be further coupled to one or more database(s) 220, such as stored ina data storage device 250), switching center(s) 125 (including mobileswitching centers), and wireless base station(s) 130, such as forcommunication to a mobile or cellular client device 300, 300A. Forexample, the network 110 may be the Internet, or a public or private LANor WAN.

A user can connect to the search engine 225 within a server system 200to submit a query and receive search results, as discussed in greaterdetail below with reference to FIG. 6 . When the user submits the querythrough a user input device 345 attached to or forming part of a clientdevice 300, 300A (such as a keyboard, a touch screen, a mouse, etc.), aclient-side query signal is sent into a network 110 and is forwarded tothe server system 200 as a server-side query signal. Server system 200can be one or more server devices in one or more locations. A serverdevice 200 includes a processor 210, which can include the search engine225 loaded therein. A processor 210 is structured or otherwiseprogrammed to process instructions within the server system (orapparatus) 200. These instructions can implement one or more componentsof the search engine 225. The processor 210 can be a single-threadedprocessor or a multi-threaded processor, and can include multipleprocessing cores 212. The processor 210 can process instructions storedin the memory 205 related to the search engine 225 and can sendinformation to the client devices 300, 300A, through the network 110, tocreate a graphical presentation in a user interface of the client device300, 300A (e.g., a search results web page displayed in a web browser,such as using HTML, XML, Javascript, etc., alone or in combination witheach other), such as those illustrated and discussed below withreference to FIGS. 7-9 .

For example, in system 100, a server system (or apparatus) 200 may beutilized to provide the personalization of search results and searchresult ranking in a search engine, interactively with a client device300, 300A such as a computer or mobile smartphone, via network 110(e.g., Internet). For such an embodiment, and as discussed in greaterdetail below with reference to FIGS. 7-9 , a series of graphical userinterfaces 700, 805, 810, 900, 930 are displayed on a client device 300,300A, such as a computer or smartphone, with the user inputtinginformation and making the various parameter selections described belowvia the series or succession of displayed graphical user interfaces 700,805, 810, 900, 930 in the client device 300, 300A, which information andparameter selections are then transmitted to the server system (orapparatus) 200. In turn, the personalization of search results,customized filtering and personalization of search result ranking isperformed by the server system (or apparatus) 200, using a search engine225 (described below) and provides the personalization of search resultsand search result ranking (such as in the form of an HTML XML orscripting file) to the client device 300, 300A for display and selectionvia the one or more graphical user interfaces 700, 805, 810, 900, 930.

For example, representative embodiments of the subject matter describedin this specification may be implemented in a computing system thatincludes a back-end or a middleware component, such as a server systemor apparatus 200, or that includes a front-end component such as clientdevices 300, 300A having an interactive graphical user interface or aweb browser (either or both of which may display the graphical userinterfaces 700, 805, 810, 900, 930), or any combination of one or moresuch back-end, middleware, or front-end components. The components ofthe system can be interconnected by any form or medium of digital datacommunication, such as the illustrated network or internet 110 as arepresentative communication network (e.g., a local area network (“LAN”)and a wide area network (“WAN”), an inter-network such as the Internet,and/or a peer-to-peer network. The server system or apparatus 200 andthe client devices 300, 300A may utilize various different computingmodel infrastructures, such as web services, distributed computing andgrid computing infrastructures, for example and without limitation.

Referring to FIG. 2 , the exemplary or representative server system (orapparatus) 200 comprises one or more processor(s) 210, a networkinput/output (“I/O”) interface 215, and a memory 205 (such as a randomaccess memory (RAM) or the other forms of memory 205 described below,such as DRAM, SDRAM, etc., which also may include one or more databases220A), and may be coupled to one or more additional data storage devices250 for database 220 storage. Depending upon the selected embodiment,the server system or apparatus 200 may also include optional componentsdiscussed below with reference to client devices 300, 300A, such as userinput devices, for example. The processor(s) 210, network input/output(“I/O”) interface 215, memory 205, and data storage device 250 having adatabase 220, may be implemented or embodied as known or becomes knownin the electronic arts, with various examples described in greaterdetail below. In addition, multiple server systems (or apparatuses) 200may be utilized in a search system 100, for example and withoutlimitation.

FIG. 3 is a block diagram illustrating an exemplary or representativeclient device (or apparatus) 300, 300A. Referring to FIG. 3 , theexemplary client device 300, 300A comprises one or more processor(s)305, a network input/output (“I/O”) interface 315, and a memory 310(such as a random access memory (RAM) or the other forms of memory 310described below, such as DRAM, SDRAM, etc., which also may include oneor more databases). Depending upon the selected embodiment, the clientdevice 300, 300A may also include optional components such as a userinput/output (“I/O”) interface 320 (such as for coupling to a user inputdevice 345 such as a keyboard, computer mouse, or other user inputdevice, not separately illustrated), an audio input/output interface 325(e.g., for coupling to a microphone 355, to audio speakers or otheraudio player 330, such as for auditory output of results), a displayinterface or controller 335, and may also include a display 340, such asan LED or LCD screen or mobile smartphone touch screen, such as fordisplay of the various graphical user interfaces 700, 805, 810, 900,930. The processor(s) 305, network input/output (“I/O”) interface 315,memory 310, user input/output (“I/O”) interface 320, audio input/outputinterface 325, audio player 330, display interface or controller 335,and display 340, may be implemented or embodied as known or becomesknown in the electronic arts, with various examples described in greaterdetail below. In addition, multiple client devices 300, 300A may beutilized in a system 100, for example and without limitation. Theprocessor 305 is structured or otherwise programmed to processinstructions within the system 100. In various embodiments, theprocessor 305 is a single-threaded processor or may be a multi-threadedprocessor, and may have a single processing core or multiple processingcores (not separately illustrated). The processor 305 is structured orotherwise programmed generally to process instructions stored in thememory 310 (or other memory and/or a storage device included with theclient device 300, 300A) to display graphical information for a userinterface.

Referring again to FIG. 2 , the search engine 225 of the processor(s)210 of the server system (or apparatus) 200 receives (via network I/Ointerface 215) the server-side query signal transmitted from a clientdevice 300, 300A via the network 110. The search engine 225 utilizes theinformation within the user query (as described in greater detail below)to generate and provide personalized search results, customizedfiltering, and personalized search result ranking. The search engine 225may comprise one or more of the following subsystems or components, suchas a return query generator 230, a scoring generator 235, a rankinggenerator 240, and optionally a rank modification generator 245, whichare adapted, configured or programmed to perform the personalizedsearch, scoring, customized filtering, and personalized rankingmethodologies described in greater detail below. The search engine 225,and any or all of its subsystems or components, may also be distributedbetween and among the processing cores 212 in a representativeembodiment.

As described in greater detail below, a user (referred to as a first,second, third or next (etc.) respondent), using a client device 300,300A and typically following a log in or user registration process,generates one or more client-side queries 605 transmitted via thenetwork 110 and received by the server system 200 as one or moreserver-side queries 610, or referred to herein more simply as a query orqueries 605, 610. In a representative embodiment, the one or moreclient-side queries 605 are selected from a drop down menu 710 providedin a graphical user interface 700 (illustrated in FIG. 7 ), for exampleand without limitation. In another representative embodiment, the usermay input the query or queries 605, 610, and the search engine 225 (andmore particularly the return query generator 230) will utilize the query605, 610 (e.g., through keywords such as “electrical”, “automotive”,“engineer”, and so on, also for example and without limitation), asdiscussed in greater detail below. These one or more client-side queries605 are then transmitted (via network 110) to the search engine 225(e.g., by the user clicking on the NEXT button 720 in the GUI 700, forexample). For online employment compatibility search, matching andranking, such a client-side query 605 may be a job title or occupationor employment position, for example and without limitation. For othertypes of online searching, matching and ranking, the client-side query605 may take other forms, such as a selection of geographic regions forsearching for real estate such as a house, apartment, condominium,cooperative, etc., or searching for a spouse or companion, or searchingfor other types of personalized information (e.g., automobile,professional, book, music or clothing recommendations), all for exampleand without limitation.

Also as described in greater detail below, instead of returning searchresults in response to the query 605, 610, the return query generator230 of the search engine 225 of the server system 200 retrieves (frommemory 205 and/or from database 220 stored in data storage device 250)and transmits a plurality of “return queries” (or “reverse queries”) 615(for the server-side return queries 615, transmitted via network 110 toform client-side return queries 620, and referred to herein more simplyas return queries 615, 620) to this respondent (or co-respondent). Thesereturn queries 615, 620 have at least one of several forms, and all areone or more series of questions pertaining to parameters orcharacteristics directly relevant to the client-side query 605, such asquestions directly relevant to activities which may or may not beperformed for the selected job title or occupation or employmentposition being sought, for example and without limitation. As such, thereturn queries 615, 620 may also be considered, equivalently, to be“parameter requests”, for the search engine 225 to obtain variousparameters (determined by the user) which are used to provide customizedfiltering, as discussed in greater detail below. In a representativeembodiment, the return queries 615, 620 are stored and indexed in thememory 205 and/or database 220 stored in data storage device 250, suchas indexed by job title, position, or keywords (e.g., through keywordssuch as “electrical”, “automotive”, “engineer”, and so on, also forexample and without limitation), and as a result, the return querygenerator 230 may utilize the query 605, 610 or keywords of the query605, 610 to access the memory 205 and/or database 220 stored in datastorage device 250 and retrieve the return queries 615, 620corresponding to the indexed query 605, 610 and/or its keywords. Such anindex system in the memory 205 and/or database 220 may have any numberof forms and structures, as known or becomes known in the art, such as aseries of database tables, look-up tables, etc.

As a corollary, the server system 200 is also receiving client-sidequeries 605 (as server-side queries 610) from client devices 300, 300Afrom other sources, such as from companies who are offering employmentopportunities and looking to add employees or contractors, from realestate sellers and agents, from vendors, etc., for example and withoutlimitation. To distinguish these users, they are referred to herein asfirst, second, third or next (etc.) co-respondents, for the pair-wisescoring described in greater detail below; those having skill in the artwill recognize that depending upon the circumstances or situation, anygiven user may be a respondent or co-respondent, or all may simply berespondents or co-respondents (e.g., in a social matching application,all may be respondents, or may be categorized as respondents andco-respondents based on gender and preferences, also for example andwithout limitation). Again, instead of returning search results inresponse to the query 605, 610, the return query generator 230 of thesearch engine 225 of the server system 200 generates and transmits aplurality of return queries 615, 620 to this (first or next)co-respondent. These return queries 615, 620 are also one or more seriesof questions pertaining to (corollary) parameters or characteristicsdirectly relevant to the client-side query 605, such as questionsdirectly relevant to activities which may or may not be performed forthe selected job title or occupation or employment position beingoffered, or to social or sports activities (e.g., theatre, football,skiing) which may or may not be performed for social engagements, forexample and without limitation.

In a representative embodiment, the return queries 615, 620 to therespondent and to the co-respondent pertain to identical or similarinformation, but from different, corresponding (or corollary)perspectives. For example, from a first perspective (e.g., a respondentperspective), a return query 615, 620 may ask (and present a slidingscale on a graphical user interface for a response) how much the userenjoys or wants to do a typical employment activity, e.g., for amechanical engineer, how much the user likes (or dislikes) stress andstrain analysis. Also for example, from a second, corollary perspective(e.g., a co-respondent perspective), a return query 615, 620 may ask(and present a sliding scale on a graphical user interface for aresponse) how much time the user expects the employment candidate to beengaged in a typical employment activity, e.g., for a mechanicalengineer, how much time the employee will be expected to perform stressand strain analysis. As such, the return queries 615, 620 to therespondents and co-respondents, for any given client-side query 605,pertain to identical or similar types of information, so that theanswers or responses to the return queries 615, 620 of the respondentsand co-respondents may be directly compared and scored, as discussed ingreater detail below, to provide customized filtering andpersonalization of search results and search result ranking.

Similarly, suitable return queries 615, 620 are available for othertypes of online searching, filtering, matching and ranking, such assearching for real estate, searching for a spouse or companion, orsearching for other types of personalized information (e.g., automobile,book, music or clothing recommendations). For example and withoutlimitation, such return queries 615, 620 may pertain to expected commutetimes, access to public transportation, job availability, entertainmentavailability, and expected quality of the public schools in a geographicregion, for real estate searching. Those having skill in the art willrecognize that innumerable return queries 615, 620 may be developed anddeployed for any selected search topic for customized filtering andpersonalization of search results and search result ranking, and allsuch return queries 615, 620 are within the scope of this disclosure.For example and without limitation, a representative embodiment of asearch engine 225, representative client-side queries 605, andrepresentative corresponding return queries 615, 620 for employmentcontexts are available at http://www.workfountain.com. Similarly,various other public sources provide significant databases (available tothe public) having listings of hundreds to thousands of job titles and,for each such job title, relevant activities to be performed and skillsrequired for that selected job title (e.g., a listing of 50-100 relevantactivities to be performed and skills required for that selected jobtitle, for thousands of different job titles), such as available throughO*NET under the sponsorship of the U.S. Department of Labor/Employmentand Training Administration (USDOL/ETA), at https://www.onetonline.org.For other applications of the personalization of search results andsearch result ranking disclosed herein, suitable return queries 615, 620may be obtained or derived from a wide variety of public and private orsubscription sources, such as multiple listing services for real estate,also for example and without limitation.

Those having skill in the art will recognize that any and all suchreturn queries 615, 620 are likely to vary over time and be indexed inthe memory 205 and/or database 220 for entirely new keywords, positionsor job titles, as entirely new fields and employment opportunities aredeveloped and implemented, and correspondingly return queries 615, 620may be developed and deployed for any selected search topic forpersonalization of search results, customized filtering, andpersonalization of search result ranking. Innumerable examples ofrecently developed new fields are available, and might includesmartphone application development, online music download development,social media web development, and ebook websites, and so on.

As mentioned above, in another representative embodiment, instead ofselecting one or more queries 605, 610 (such as from a drop down menu710) the user may input their own, personal query or queries 605, 610,and the search engine 225 (and more particularly the return querygenerator 230) will utilize the query 605, 610 (e.g., through keywordssuch as “electrical”, “automotive”, “engineer”, and so on, also forexample and without limitation), as mentioned above, to access thememory 205 and/or database 220 and retrieve the return queries 615, 620corresponding to the indexed query 605, 610 and/or its keywords.

In addition, as one aspect of the artificial intelligence of therepresentative embodiments, the search engine 225 may also suggestadditional queries 605, 610 to the user, such as queries 605, 610 forrelated fields and occupations, e.g., suggesting automotive engineeringto a mechanical or aerospace engineer, or suggesting the occupation ofcounty commissioner to a lawyer, for example and without limitation. Inaddition, in many such fields, there may be a substantial overlap ofreturn queries applicable to these various, different but relatedfields, such as to the different engineering fields, for example. Inanother representative embodiment, such related fields may becross-indexed or otherwise flagged, such that the return query generator230 may automatically generate and push additional return queries 615,620 to the respondent or co-respondent that would or might be includedin response to queries 605, 610 for these related fields, but which maynot be otherwise included in return queries 615, 620 pertaining to anyselected, initial query 605, 610, such as automatically generatingadditional return queries 615, 620 pertinent to automotive engineeringin response to a different query related to mechanical or aerospaceengineering, also for example.

As discussed in greater detail below, the responses to the returnqueries 615, 620 (equivalently referred to as parameter requests 615,620), received by the server system 200 from the various respondents andco-respondents, may be considered to be customizable parameters, and maybe utilized by one or more processors 210 to form correspondingrespondent customized digital filters (350, 350 _(A), 370, 370 _(A)) andco-respondent customized digital filters (360, 360 _(A)) which, in turn,may be utilized for personalization of search results andpersonalization of search result ranking. The resulting digital filters(350, 350 _(A), 360, 360 _(A), 370, 370 _(A)) are then stored by the oneor more processors 210 of the server system 200 centrally in a datastorage device 250 and/or a memory 205, for example and withoutlimitation. Such customized filters are discussed in greater detailbelow with reference to FIGS. 11 and 12 .

Also discussed in greater detail below, based upon responses(parameters) to the return queries 615, 620 received by the serversystem 200 from the various respondents and co-respondents, as the casemay be, the server system 200 performs a pair-wise scoring (pair-wise“goodness of fit” or “alignment” scoring), typically across all (ormost) of the relevant combinations of respondent-co-respondent pairs (ormore simply, respondent pairs), and using the pair-wise scoring, returnspersonalized and ranked search results. Such pair-wise scoring acrossall (or most) of the relevant combinations of respondent-co-respondentpairs may also be performed equivalently using the respondent customizeddigital filters (350, 350 _(A), 370, 370 _(A)) and co-respondentcustomized digital filters (360, 360 _(A)), also to return personalizedand ranked search results, as discussed in greater detail below.

For an employment context, from an employer point of view, instead ofreceiving hundreds or thousands of resumes, these personalized andranked search results translate to receiving ranked results ofindividual candidates seeking a given employment opportunity and havingthe highest pair-wise scorings for that employer, indicating that theyare the candidates most likely to fit and succeed in the employmentopportunity, resulting in a huge savings of time in reviewingcandidates/resumes, a reduction in turnover costs, and a dramaticreduction in the amount of time required to fill a selected position oremployment opportunity with a qualified candidate whose interests andgoals align with those of the employer. Similarly, from an employmentcandidate point of view, these personalized and ranked search resultstranslate to receiving ranked results of companies offering a givenemployment opportunity and having the highest pair-wise scorings forthat individual candidate, indicating that they are offering a jobopportunity in which the candidate is most likely to fit and succeed, ina position providing activities meeting the individual candidate'sinterests and career goals.

FIG. 4 is a flow diagram illustrating an exemplary or representativemethod embodiment for personalization of search results and searchresult ranking in a search engine. FIG. 5 is a flow diagram illustratingan exemplary or representative method embodiment for pair-wise (ordyadic) score determination for personalization of search results andsearch result ranking in a search engine. FIG. 6 is a block and flowdiagram illustrating exemplary or representative message transmissionsequences for personalization of search results and search resultranking in a search engine. FIG. 7 is a diagram illustrating anexemplary or representative graphical user interface providinguser-selectable initial client-side queries 605. FIGS. 8A and 8B arepaired respondent and co-respondent diagrams illustrating exemplary orrepresentative graphical user interfaces illustrating representativereturn queries 615, 620 providing user-selectable parameters forpair-wise score determination for personalization of search results andsearch result ranking in a search engine. FIGS. 9A and 9B are sequencesof diagrams illustrating exemplary or representative graphical userinterfaces, respectively, for output of personalized search results andsearch result ranking, and for follow on communications.

In a representative embodiment, a user (respondent or co-respondent)typically registers with the search provider, such as Workfountain(www.workfountain.com) and establishes a free account, for severalreasons, including to enable push notifications (and select the type(s)of notifications, such as text, email, etc.) when personalized andranked search results are returned, and to enable the user to access thesearch results and communicate with a candidate or prospective employer,for example and without limitation. Additional information may also becollected at that time or any other time (e.g., at the end) in theprocess of responding to the return queries 615, 620, depending on theselected embodiment. For example, for applications of thepersonalization of search results and search result ranking inemployment contexts, additional information may be collected, also via aseries of graphical user interfaces (not separately illustrated), suchas colleges and universities attended, degrees received, citizenship,current and past employers, and other talents, such as fluency in one ormore languages, skill or fluency in various software programs, anddemographics (which are useful for compliance determinations andreporting), along with opportunities to upload various documents, suchas resumes, for example and without limitation. Any number of theseattributes may be utilized for additional filtering as well to createthe personalized search results and search result rankings, such asadditional, user-selectable filtering to block a current or pastemployer or employee from pair-wise scoring and thereby block thepossible matching to one another in the results returned, or toadditionally filter based on geographic regions, language skills,citizenship, visa status, college degree level, for example and withoutlimitation. With entry of this information, along with responding to thereturn queries 615, 620 (equivalently, providing parameters) describedbelow, the user will have completed a user profile which, depending uponthe outcome of the personalization of search results and search resultranking process, may be provided to a prospective employer or aprospective candidate, for example, as part of the search results andsearch result ranking returned to the user.

Referring to FIG. 4 and the other Figures, an exemplary orrepresentative method embodiment for personalization of search resultsand search result ranking in a search engine 225 begins, start step 400,with a reception of one or more queries 605, 610 by the search engine225, or the presentation by the server system 200 of a plurality ofselectable queries 605, 610 to the user, such as via the graphical userinterface illustrated in FIG. 7 , and then reception of one or morequeries 605, 610 by the search engine 225. For example, as illustratedin FIG. 7 , one or more queries 605 are illustrated, both via drop downmenus and via entry by the user into a blank search space (for a query605 _(A), “Search Job Titles”) of graphical user interface 700.

The return query generator 230 then utilizes the received one or morequeries 605, 610, either using keywords from the one or more queries605, 610 or by the one or more queries 605, 610 already having beendirectly indexed into the memory 205 and/or database 220, to retrieve aplurality of return queries 615, 620 stored in the memory 205 and/ordatabase 220, step 405, and transmit (or return) the first (or next)series of the return queries 615, 620 to the user via network 110. Asmentioned above, in a representative embodiment, the return queries 615,620 are typically user preference questions concerning characteristics,traits, or features which are directly relevant to the search, such asthose illustrated in FIGS. 8A and 8B. As the user (respondent orco-respondent) provides responses to the return queries 615, 620 (usingNEXT button(s) 845, 850 of GUIs 805, 810) which are received by thesearch engine 225, step 415, depending upon the particular search andnumber of relevant return queries 615, 620, when additional returnqueries 615, 620 are available, step 420, the method iterates, returningto step 410 and transmitting the additional return queries 615, 620 andreceiving the additional responses, steps 410 and 415. (In the eventresponses are not received in step 415, depending on the selectedembodiment, the search engine 225 may proceed to step 420 if responseshave been received previously, or proceed to step 465 to determine ifadditional queries 605, 610 have been received, for example). Asindicated above, in a representative embodiment, as many as fifty ormore return queries 615, 620 may be available for a given search, suchas for a selected job title or employment opportunity. In addition,multiple queries 605, 610 may have been received in step 400, typicallyresulting in a larger number of return queries 615, 620 provided to theuser (respondent or co-respondent) by the return query generator 230 ofthe search engine 225.

The return queries 615, 620 are typically provided to a user via agraphical user interface, such as the graphical user interfaces 805 and810 illustrated in FIGS. 8A and 8B, with each return query 615, 620typically generated and provided using a graphical scale or continuum inrepresentative embodiments, which in turn provides that thecorresponding responses are directly quantifiable, such as by numericalvalues, and can be directly compared and normalized or otherwisemodified (e.g., comparing the numerical values provided by a respondentand co-respondent when that user clicks on the scale or continuum).There are typically at least two or more types of scale or continuumreturn queries 615, 620, those which are balanced, such as the scaledquestions 820, 830 illustrated in FIGS. 8A and 8B, ranging from a −5 toa +5 and centered at zero (0), and unbalanced, such as the scaledquestions 815, 825 illustrated in FIGS. 8A and 8B, ranging from zero (0)to a +20 or more. In addition, various return queries 615, 620 may alsobe provided for yes or no responses, as dichotomous variables, such asyes/no question 860 (e.g., for responses presented as radio buttons 865in a GUI 805, 810), such as “do you have a bachelors degree?” asillustrated, or others such as“did you go to college?” or “is a collegedegree necessary for this position?”, etc. Those having skill in the artwill recognize that any balanced scale can be converted to an unbalancedscale, and vice-versa, without affecting any of the pair-wise scoringcalculations and search determinations herein. Those having skill in theart will also recognize that many other question and query types andformats are available which can be converted into quantifiable ornumerical values for use in scoring and/or filtering, any and all ofwhich are considered equivalent and within scope of the disclosure. Asillustrated in FIG. 8A, as known in the art, a user will click(typically through a pointing device such as a mouse, trackpad,touchscreen or other user input device 345) on the scale of the returnquery 615, 620, which typically results in buttons (such as a radiobuttons) 835 appearing in the selected locations as shown. Asillustrated in FIG. 8B, as known in the art, a user will move sliderbuttons or devices 840 (also typically through a pointing device such asa mouse, trackpad, touchscreen or other user input device 345) on thescale of the return query 615, 620, to the selected location as shown.Regardless of implementation, both graphical user interface 805, 810implementations provide the capability for a user to respond to thereturn queries 615, 620 and have a quantifiable or numerical result, asa customized parameter, returned to the search engine 225 in step 415(also illustrated as client- and server-side messages 625, 630 in FIG. 6).

FIGS. 8A and 8B also illustrate the different, corresponding (orcorollary) perspectives of the return queries 615, 620 for a respondentand a co-respondent pertaining to different goals or interests which maybe relevant, for example, in an employment context. For example, asillustrated in FIG. 8A, balanced, scaled question 820 asks a respondent(from a first perspective) to indicate how much interest the respondenthas in designing telecommunication equipment, and providing for answersranging from “not interested” (numerical value of −5), “not mypreference but willing to” (numerical value of −2.5), “not my favoritebut don't dislike it” (numerical value of 0, i.e., a “don't care”),“interested” (numerical value of +2.5), and “very interested” (numericalvalue of +5), and yes/no question 860 asks a respondent if he or she hasa bachelors degree. Continuing with the example, as illustrated in FIG.8B, balanced, scaled question 830 asks a co-respondent (from a secondperspective, such as an employer perspective) to indicate how much timethe co-respondent (e.g., the employer) would expect a candidate to spenddesigning telecommunication equipment, and providing for answers rangingfrom “not at all” (numerical value of −5), “not often, if ever”(numerical value of −2.5), “occasionally” (numerical value of 0),“often” (numerical value of +2.5), and “most of the time” (numericalvalue of +5), and yes/no question 870 asks the co-respondent if abachelors degree is required for the position. Those having skill in theart will recognize that a respondent who is very interested in designingtelecommunication equipment, all other things being equal (such as thedegree of alignment on other relevant responses), may be a very goodcandidate for a company seeking an engineer who will spend most of hisor her time designing telecommunication equipment. Accordingly, thereturn queries 615, 620 are directly corresponding, as corollaries orparallel, related questions, resulting in the responses to the returnqueries 615, 620 for a respondent and a co-respondent being directlycomparable and commensurate, which can be utilized (as quantized,numerical values or customized parameters) by the scoring generator 235directly in the pair-wise scoring and/or customized digital filteringprocesses discussed in greater detail below.

Those having skill in the art will recognize that return queries 615,620 for a respondent and a co-respondent may be developed similarly, notmerely for other types of employment opportunities, but also for otherfields, topics, and areas of interest for personalization of searchresults and search result ranking in a search engine 225, such as forreal estate searching, for example and without limitation. For example,a representative balanced, scaled question may ask a respondent (from afirst perspective) to indicate how much interest the respondent has inpurchasing a home in a highly ranked school district, and also providingfor answers ranging from “not interested” (numerical value of −5), “notmy preference but willing to” (numerical value of −2.5), “not myfavorite but don't dislike it” (numerical value of 0, i.e., a “don'tcare”), “interested” (numerical value of +2.5), and “very interested”(numerical value of +5). Continuing with the example, a representativebalanced, scaled question may ask a co-respondent (from a secondperspective, such as a real estate agent or seller perspective) toindicate the ranking of the local school(s), and providing for answersranging from “not respected at all” (numerical value of −5), “otherschools available” (numerical value of −2.5), “acceptable” (numericalvalue of 0), “respected” (numerical value of +2.5), and “highlyrespected” (numerical value of +5). Those having skill in the art willrecognize that a respondent who is very interested in sending his/herchildren to a local magnet school, all other things being equal, may bea very good candidate for a seller having a home in such a neighborhood.Accordingly, the responses to the return queries 615, 620 for arespondent and a co-respondent are directly comparable and commensurateacross a wide range of potential searches, and can be utilized (asquantized, numerical values) by the scoring generator 235 directly inthe pair-wise scoring and/or customized digital filtering processesdiscussed in greater detail below.

Those having skill in the art will also recognize that as the serversystem 200 is utilized repeatedly for personalization of search resultsand search result ranking, the memory 205 and/or database 220 willbecome populated with responses to the return queries 615, 620 for aplurality of respondents and co-respondents. In addition, the responsesto the return queries 615, 620 may be represented numerically (ormathematically) in a wide variety of ways, such as by a plurality ofnumerical values forming a customized digital filter (e.g., as a matrix350, 360, or a vector 350 _(A), 360 _(A), 370 _(A)), or otherwise bynumerical values for a selected employment opportunity or other searchtopic of interest (e.g., [−5, 0, +2.5, +5, . . . ]). Similarly, theremay be occasions when a respondent or co-respondent does not respond toa particular return query 615, 620 and, in that event, that particularreturn query either can be removed from the scoring process (unscored)or a “don't care” (e.g., a zero value of a balanced scale) can beinserted, for example and without limitation, and any and all suchvariations are considered equivalent and within the scope of thedisclosure.

Referring again to FIG. 4 , following steps 410-420, with the responsesto the return queries 615, 620 (from a respondent or co-respondent)having been received, in step 425, the search engine 225 proceeds tosearch the memory 205 and/or database 220 for one or more sets ofindexed, corresponding (preference or parameter) responses, i.e.,searching the responses to the return queries 615, 620 from one or moreco-respondents or respondents, respectively, such as searching thememory 205 and/or database 220 for candidate responses to the returnqueries 615, 620 for a selected employment opportunity (as a respondent)to be used in scoring against the responses to the return queries 615,620 submitted by an employer offering the selected employmentopportunity (as a co-respondent).

In representative embodiments, the scoring generator 235 then comparesthe responses to the return queries 615, 620, from a selectedcombination of a selected respondent and a selected co-respondent, step430, such as the scoring generator 235 then determining a comparative,pair-wise (or dyadic) score (a pair-wise “goodness of fit” or“alignment” score) for a selected respondent-co-respondent combinationor pair, using any of the various methods disclosed herein and theirequivalents. As there may be many such respondent-co-respondentcombinations relevant or related to any particular user query 605, 610,when there are any such additional combinations remaining for scoring instep 435, the scoring generator 235 iterates, returning to step 430 togenerate or determine the next pair-wise score for the nextrespondent-co-respondent combination, typically iterating across all (ormost) of the relevant combinations of respondent-co-respondent pairs (ormore simply, respondent pairs, depending on the selected embodiment),generating a plurality of pair-wise scores, one such score for everyrelevant respondent-co-respondent pair (that has not been eliminated asa possible combination due to additional filtering, for example, of acurrent employer or employee). For example, an employer “A” may beseeking candidates for a position “Q”, an employer “B” may be seekingcandidates for a similar position “Q”, and 25 potential candidates (1,2, 3, . . . , 25) are seeking such a “Q” employment opportunity. Alsofor example, an employer “C” may be seeking candidates for a position“R”, and 15 other potential candidates (26, 27, 28, . . . , 40) areseeking such an “R” employment opportunity. Accordingly, the scoringgenerator 235 will generate, in these two examples, a total of 65pair-wise scores, for every relevant respondent-co-respondent pair,resulting in: (1) for the “A” combinations, a score for the combinationA-1, another score for the combination A-2, another score for thecombination A-3, etc., through another score for the combination A-25;(2) for the “B” combinations, a score for the combination B-1, anotherscore for the combination B-2, another score for the combination B-3,etc., through another score for the combination B-25; and (3) for the“C” combinations, a score for the combination C-26, another score forthe combination C-27, another score for the combination C-28, etc.,through another score for the combination C-40. To the extent that the“Q” and “R” employment opportunities may overlap, and have overlappingreturn queries 615, 620 and responses to the return queries 615, 620,additional pair-wise scores may be generated as well, such as for allremaining pair-wise scores covering all dyadic combinations of the “Q”and “R” employment opportunities with the potential candidates (1, 2, 3,. . . , 40). A wide variety of pair-wise scoring methods are available,with a representative scoring embodiment discussed in greater detailbelow with reference to FIG. 5 , and another embodiment using customizeddigital filters, discussed in greater detail below with reference toFIG. 12 .

Following generation or determination of the pair-wise alignment scoresfor each combination of respondent and co-respondent (in steps 430 and435), the ranking generator 240 sorts and ranks (or re-sorts andre-ranks) each of these respondent-co-respondent combinations accordingto the corresponding pair-wise alignment score for the selectedcombination, step 440, typically ranked from highest to lowest, and doesso for each of the respondents and co-respondents. Continuing with theexample above, a representative sorting and ranking will be generatedfor a selected co-respondent (e.g., an employer) “A”, as respondent 3(score for the A-3 combination equal to 90%), followed by respondent 6(score for the A-6 combination equal to 85%), etc., while another (andtypically different) sorting and ranking will be generated for each ofthe respondents (e.g., candidates). Continuing again with the exampleabove, a representative sorting and ranking will be generated forrespondent 3, as co-respondent B (score for the B-3 combination equal to95%) followed by respondent A (score for the A-3 combination equal to90%), etc., while a representative sorting and ranking will be generatedfor respondent 8, as co-respondent A (score for the A-8 combinationequal to 75%) followed by respondent B (score for the B-8 combinationequal to 70%), etc. This also shows that the various sorting and rankingcan result in each respondent and co-respondent having different searchresults and rankings; for example, respondent 3 is highest ranked inemployer A's ranking, but the converse is not true, with employer Bbeing ranked higher in respondent 3's ranking. In addition, for thelatter example for respondent 8, having comparatively lower alignmentscores, it is possible that depending upon the number of personalizedresults and rankings provided to the relevant co-respondents, respondent8 might not be included in one or more (or any) of the co-respondentssearch results and rankings (having alignment scores, for example, belowa predetermined threshold or cut-off).

Using this pair-wise (or dyadic) alignment scoring for every suchrelevant combination, it can be seen that following sorting and rankingby the alignment scores, the search engine 225 has generatedpersonalized search results and search result rankings, as a mutualgoodness of fit between each combination of respondents andco-respondents.

Following the sorting and ranking of step 440, the personalized searchresults and search result rankings (or a predetermined number or amountof personalized search results and search result rankings) areadditionally filtered by the search engine 225, step 445, when indicatedin the user profile information of a respondent or a co-respondent asmentioned above, such as to block or eliminate potential search results,e.g., to block or eliminate a ranked search result (combination) of acurrent employee and current employer, for example and withoutlimitation. The (filtered) personalized search results and search resultrankings are output by the search engine 225 to each of the respondentsand co-respondents, step 450, such as through the representative,personalized, sorted and ranked listing 910 of candidates shown on GUI900 illustrated in FIG. 9A. In representative embodiments, the searchengine 225 may also and preferably does provide one or more pushnotifications (e.g., a text or an email, for example, to a computer or asmartphone of a respondent or a co-respondent) of the personalizedsearch results and search result ranking, step 455 (also illustrated asserver- and client side messages 635, 640 and server- and client sidepush notification messages 645, 650 in FIG. 6 ).

The significance of this latter notification step should not beunderestimated, as these personalized search results and search resultrankings may be quite time-sensitive, especially for searches inselected areas, such as employment and real estate, for example (e.g.,employment opportunities may get “stale” quickly as positions get filledor candidates accept other employment opportunities, especially incompetitive environments), and a respondent or a co-respondent may wantto be notified immediately of pertinent or relevant search results. Forexample, a candidate may want an immediate notification to promptlyrespond to a good employment opportunity, while an employer may want animmediate notification to promptly respond to and recruit a goodcandidate, e.g., to be able to respond appropriately and timely beforesomeone else might respond to the opportunity or recruit the candidate.Also for example, in other contexts such as real estate searching, arespondent or a co-respondent may want to be notified immediately ofpertinent or relevant search results, to place an offer for a propertyof a new real estate listing, such as in a “hot” real estate market.

The personalized search results and search result rankings are alsostored by the search engine 225 in the memory 205 and/or database 220,step 460. When additional queries 605, 610 are received, step 465, thesearch engine 225 will iterate, returning to step 405, and otherwise themethod may end, return step 470.

Those having skill in the art will recognize that the steps indicated inFIG. 4 (and in FIG. 5 ) may be performed in a wide variety of orders,for example, additional filtering step 445 may be performed at any timeprior to outputting the personalized search results and search resultrankings. In addition, many of the steps and processes may be performedby the search engine 225 in parallel or multithreaded, particularly inmulticore embodiments, such as the scoring of each respondent andco-respondent combination assigned to a different core 212 in amulticore processor 210, or performed simultaneously and in parallelacross different queries 605, 610 and corresponding return queries 615,620 or for different respondents and co-respondents, with all suchvariations considered equivalent and within the scope of the presentdisclosure.

As new queries 605, 610 are received by the search engine 225, it shouldalso be noted that the personalized search results and search resultrankings are likely to change. For example, new candidates foremployment opportunities and new employers offering employmentopportunities all may be participating in this process, and as eachrespondent or co-respondent provides new queries 605, 610 to the searchengine 225 and responds to the return queries 615, 620, new personalizedsearch results and search result rankings may be generated, depending ofcourse upon the actual pair-wise scores generated by each new availablecombination of respondents and co-respondents. In a representativeembodiment, however, this “churning” of personalized search results andsearch result rankings does not have to be calculated or re-calculatedfor all respondents in the memory 205 or database 220 every time theremay be a change; for example and without limitation, the redeterminationof personalized search results and search result rankings may bedeferred until a user (respondent or co-respondent) logs into the serversystem 200, or the redeterminations of the personalized search resultsand search result rankings may simply be performed at regular intervals,e.g., 3 a.m. when it is less likely that respondents and co-respondentswill be accessing the server system 200, or periodically throughout theday. With each such generation of new personalized search results andsearch result rankings, additional push notifications are also typicallygenerated (step 455) to the various respondents and co-respondents.

In addition, as new candidates for employment opportunities and newemployers offering employment opportunities all may be participating inthis process, various other candidates or employers may have left orwithdrawn from the search process, effectively becoming inactive.Nonetheless, in representative embodiments, the responses to the returnqueries 615, 620, as user-determined characteristics oruser-customizable parameters, may persist over time within the system200, for any predetermined amount of time, and can continue to beutilized in the search process, continuing to generate new personalizedsearch results and search result rankings, with additional pushnotifications also being generated (step 455) to various respondents andco-respondents that may no longer be active. This may be especiallyvaluable when available employment opportunities have not been filled,but potential candidates may have withdrawn from the search process andmay be unaware of such an opportunity that they would otherwise beseeking.

Also in addition, the selection of respondent and co-respondentcombinations may be selectively varied, such that not all respondentsand co-respondents are utilized in generating personalized searchresults and search result rankings. In a representative embodiment, some“pools” of respondents and co-respondents available for use in providingpersonalized search results and search result rankings may be limited,or initially limited and then expanded after a predetermined interval oftime. For example, some employers, as co-respondents, may requestexclusive access to a selected pool of potential employment candidates,as respondents, for a predetermined period of time, for generation ofpersonalized search results and search result rankings.

In addition, the personalized search results and search result rankingsmay also be modified by a respondent or co-respondent, in any selectedtime frame (e.g., immediately, a thirty-day delay, etc.), and thismodification may also be prompted by the various representativeembodiments, as discussed below. For example, an employer or seller ofreal estate may not have received any personalized search results andsearch result rankings above a predetermined alignment score threshold.In that event, the respondent or co-respondent may repeat the process ofresponding to the return queries 615, 620, to adjust the variouspreference parameters (resulting in modifying (or decreasing the amountof) any constraints, such as the amount of time to be expected onvarious projects), with the search engine 225 (and/or rank modificationgenerator 245) then repeating the scoring, sorting and rankingprocesses, and generating a new set of personalized and ranked searchresults. This also underscores the importance of the push notificationswith the time-sensitivity of the information returned in thepersonalized and ranked search results, as it is entirely possible thata respondent or co-respondent has taken or eliminated an employmentopportunity quickly, or entered into a contract to purchase real estatein a “hot” market, all for example, thereby removing the respondent orco-respondent from the available search pool of respondents andco-respondents, resulting in fewer results to be personalized and rankedfor another co-respondent or respondent.

In addition, as another aspect of the artificial intelligence of therepresentative embodiments, the search engine 225 may also suggest thesevarious changes in the responses to the return queries 615, 620, toadjust the various preference parameters, with the search engine 225(and/or rank modification generator 245) then repeating the scoring,sorting and ranking processes, and generating a new set of personalizedand ranked search results. For example, the search engine 225 may runone or more simulations with these modified responses to the returnqueries 615, 620 (modified preference parameters, or modifiedvariables), and generate a report back to the user with the new sets ofpersonalized and ranked search results. Continuing with the example,such a report may indicate that the user would have an increased numberof employment opportunities if the user (as a potential employmentcandidate) acquired a new technical skill, such as acquiring a new skillin using a selected programming language.

Also continuing with the example, the artificial intelligence includedwithin the search engine 225 may provide additional feedback to varioususers, such as suggesting training programs to an employer. For example,such a report may indicate that the user (as a potential employer) wouldhave an increased number of internal candidates for known employmentopportunities and career promotions if the user invested in and providedtraining to the internal candidates, such as to enable the internalcandidates to acquire a new technical skill, as mentioned above. Furthercontinuing with the example, the artificial intelligence included withinthe search engine 225 may also provide these various simulations usinghistorical trend data for a user, such as an employer. The search engine225 may also run these various simulations using historical trend datafor employment opportunities, such as seasonal data, indicating that ina selected time period, the user will have additional internalemployment opportunities, and may wish to redeploy current employees tothose positions, instead of terminating their current employment andneeding to hire new employees in a few months, for example. Thisartificial intelligence of the search engine 225, therefore, has theadded benefits of a reduction in employee turnover costs, by redeployingemployees within the organization and providing internal careeradvancement opportunities, and improving the employer's competitiveadvantages.

It should also be noted that the personalized search results and searchresult rankings may be provided in a wide variety of ways; for example,the actual alignment scores for a selected combination of respondentsand co-respondents may be provided; or the alignment scores for such aselected combination of respondents and co-respondents may be translatedto a different scale (e.g., 5 stars, 4.5 stars, 4 stars, etc.) andprovided using other scaled indicators or indicia. For example, in arepresentative embodiment, such as “star” rating system is utilized andprovided (instead of the actual alignment scores) for a selectedcombination of respondents and co-respondents, as illustrated in thesorted and ranked search result listing 910 of the GUI 900 of FIG. 9A.Also in a representative embodiment, a predetermined number ofpersonalized search results and search result rankings are output instep 450, rather than all such results and rankings. In yet anotherrepresentative embodiment, a predetermined number of personalized searchresults and search result rankings are output in step 450, but through agraphical user interface, the respondent or co-respondent is given anoption to download and/or view all such results and rankings (e.g.,using the ADDITIONAL CANDIDATES button 920 of FIG. 9A).

It should be noted that the output of the personalized search resultsand search result rankings is generally provided as a listing or list ofsorted and ranked respondents or co-respondents. It should be noted thatsuch a listing or list may have any format of any kind, such assequential, a chart, a sequence of links, etc., for example and withoutlimitation. Any and all such listing or list formats are consideredequivalent and within the scope of the disclosure. Similarly, the outputof the sorted and ranked respondents or co-respondents means and is a(sorted and ranked) representation or identification of the respondentsor co-respondents, such as the name of a respondent or co-respondent,and clearly not the actual human being or company.

As mentioned above, an advantage of user registration, such as bysetting up a user profile, is that follow on communication may beestablished between the respondent and co-respondent entitiesrepresented in the personalized search results and search resultrankings. For example, as illustrated in the GUI 950 FIG. 9B, byclicking on a Request to Connect button 955, a respondent orco-respondent may contact the other, such as through an email or as aseparate communication facilitated via messaging through the serversystem 200 (such as the client- and server-side communications and/orrequest messages 655, 660 illustrated in FIG. 6 ). For example andwithout limitation, in an employment context, such additionalcommunications may include resumes, job and company descriptions, etc.

Another advantage is the ability to provide locking or “pinning”, whichcan be done by either the respondent or co-respondent to any of therespective co-respondents or respondents on their respective list, suchas by clicking on a Request to Connect button 955 of the GUIs 900, 950.Such locking or pinning allows persistence of the selected search result(and potentially also its ranking), such as of Candidate 1 illustratedin the GUI 950, regardless of the additional iterations of the searchengine 225 and resulting changes in the personalized and ranked searchresults, such that the locked or “pinned” selected search result will bemaintained in the user's sorted and ranked list 910 even as other searchresults may be deleted as higher ranked results may arrive. Inrepresentative embodiments in which a predetermined number ofpersonalized search results and search result rankings are output instep 450, such locking adds to the predetermined number, and opens up anavailable slot which may be filled by a higher or lower ranked searchresult. For example, if seven personalized search results are providedand ranked, pinning of one of the search results will keep the selectedresult in the list 910, and simultaneously allow entry of an eighthsearch result to join the list 910. This also underscores the importanceof the push notifications with the time-sensitivity of the informationreturned in the personalized and ranked search results, as an immediatelocking of a search result enables additional search results to beprovided in additional slots, and further eliminates the possibilitythat a search result will be removed or “bumped” off the ranked searchresults (of either the respondent or co-respondent) when additionalsearch results are provided.

As mentioned above, a wide variety of schema or methods are available togenerate pair-wise alignment scores for personalized search results andsearch result rankings. FIG. 5 is a flow diagram illustrating anexemplary or representative method embodiment for pair-wise (or dyadic)score determination for personalization of search results and searchresult ranking in a search engine. FIG. 10 is a diagram illustratingexemplary or representative responses of respondents and co-respondentsto return queries for pair-wise score determination for personalizationof search results and search result ranking in a search engine, and isuseful to provide examples for the pair-wise score determination(discussed with reference to FIG. 5 ). Referring to FIG. 10 , threereturn queries are illustrated as question “F” 970, question “G” 975,and question “H” 980, and because the respondent and co-respondentreturn queries 615, 620 are corollaries, they can be directly compared,as discussed above. For ease of discussion, the various responses areillustrated along the same continuums or scales, with “X” indicating theresponses of a co-respondent “D” such as an employer, “0” indicating theresponses of a first respondent “Y” (such as a first employmentcandidate), and “*” (star or asterisk) indicating the responses of asecond respondent “Z” (such as a second employment candidate).

As described above with reference to step 430 illustrated in FIG. 4 ,the scoring generator 235 compares responses of respondents andco-respondents to the preferences, parameters or characteristicsprovided in the return queries 615, 620, and generates a pair-wisealignment score utilized to provide the personalized search results andsearch result rankings, iteratively for every relevant respondent andco-respondent combination. Referring to FIG. 5 , as part of step 430,the scoring generator 235 starts, step 500, by selecting a relevantrespondent and co-respondent combination for determination of thepair-wise alignment score for that combination, step 505. For each suchcombination, there are generally or typically a plurality of responsesto the corresponding pluralities of return queries 615, 620, such as 50responses each to 50 related return queries 615, 620. The scoringgenerator 235 selects one set of responses and determines or generates araw or unmodified “distance” or variance between the responses of therespondent and the responses of the co-respondent, for each relatedreturn query 615, 620, and then for the selected respondent andco-respondent combination, their corresponding degree or percentage ofalignment, step 510.

Using the examples of FIG. 10 , for the DY combination of theco-respondent and first respondent, for the question “F” 970, theunmodified or raw distance between the responses is equal to 1. From theperspective of co-respondent D, the worst response available would havebeen a +5, a distance of 8, so the first respondent Y only differs by ⅛,generating a degree or percentage of alignment of 87.5% (=1−(⅛)), forquestion “F” 970. From the perspective of respondent Y, the worstresponse also would have been a +5, a distance of 7, so theco-respondent D only differs by 1/7, generating a degree or percentageof alignment of 85.7% (=1−( 1/7)), for question “F” 970. For the DZcombination of the co-respondent and second respondent, for the question“F” 970, the unmodified or raw distance between the responses is equalto 4. From the perspective of co-respondent D, the worst response wouldhave been a +5, a distance of 8, so the second respondent Z differs by4/8, generating a degree or percentage of alignment of 50.0% (=1−(4/8)), for question “F” 970. From the perspective of respondent Z, theworst response would have been a −5, a distance of 6, so theco-respondent D differs by 4/6, generating a degree or percentage ofalignment of 33.3% (=1−( 4/6)), for question “F” 970. Those having skillin the art will recognize that there are innumerable ways to calculatesuch distances and degrees or percentages of alignment, and all suchvariations are considered equivalent and within the scope of the presentdisclosure.

It should be noted that scoring for dichotomous variables, for yes or noresponses, will tend to result in 100% alignment or 0% alignment of theresponses of the respondent and co-respondent. As such, these responsesmay be weighted differently, or potentially given no weighting indetermining the final alignment scores, for example and withoutlimitation. Other types of responses may also be weighted differentlybased on user criteria, particularly in employment contexts, such as toflag or increase the visibility of veterans, disable persons, etc., inthe personalized search results and search result rankings.

When there are remaining responses for the selected respondent andco-respondent combination, step 515, the scoring generator 235 iterates,returning to step 510 and determines or generates a raw or unmodifieddistance or variance between the responses of the respondent andco-respondent and their corresponding degree or percentage of alignmentfor the next set of responses to the return queries 615, 620. Againusing the examples of FIG. 10 , for the DY combination of theco-respondent and first respondent, for the question “G” 975, theunmodified or raw distance between the responses is equal to 1. From theperspective of co-respondent D, the worst response would have been a −5,a distance of 7, so the first respondent Y only differs by 1/7,generating a degree or percentage of alignment of 85.7% (=1−( 1/7)), forquestion “G” 975. From the perspective of respondent Y, the worstresponse also would have been a −5, a distance of 8, so theco-respondent D only differs by ⅛, generating a degree or percentage ofalignment of 87.5% (=1−(⅛)), for question “G” 975. With the scoringgenerator 235 iterating again, and again using the examples of FIG. 10 ,for the DY combination of the co-respondent and first respondent, forthe question “H” 980, the unmodified or raw distance between theresponses is equal to 1. From the perspective of co-respondent D, theworst response would have been a +5, a distance of 7, so the firstrespondent Y only differs by 1/7, generating a degree or percentage ofalignment of 85.7% (=1−( 1/7)), for question “H” 980. From theperspective of respondent Y, the worst response also would have been a+5, a distance of 6, so the co-respondent D only differs by ⅙,generating a degree or percentage of alignment of 83.3% (=1−(⅙)), forquestion “H” 980.

Next, the scoring generator 235 determines the total raw or unmodifieddegree of alignment for the selected respondent and co-respondentcombination, step 520, which may be, for example, the sum of theindividual alignment percentages for all of the responses to the returnqueries 615, 620, or an average (e.g., mean) value of the individualalignment percentages for all of the responses to the return queries615, 620. Responses to different return queries 615, 620 may also bedifferentially weighted in various embodiments.

The scoring generator 235 then determines and generates modified ornormalized distances and corresponding modified or normalized degrees orpercentages of alignment, using modified or normalized responses, againfor the selected respondent and co-respondent combination, step 525, foreach response to a return query 615, 620. When there are remainingresponses for the selected respondent and co-respondent combination,step 530, the scoring generator 235 iterates, returning to step 525 anddetermines or generates a modified or normalized distance or variancebetween the responses of the respondent and co-respondent and theircorresponding modified or normalized degree or percentage of alignmentfor the next set of responses to the return queries 615, 620.

Again referring to the examples of FIG. 10 , those having skill in theart will recognize that the responses of the DY combination of theco-respondent and first respondent are highly aligned, differing by adistance equal to one for all responses. In many cases, such differencesmay be due to mere differences in how various individuals expressthemselves or use language, e.g., one person's “like” is anotherperson's “love”, and one person's “interested” is another person's “veryinterested”, and so on. Accordingly, in the representative embodiments,the scoring generator 235 accounts for these differences by modifying ornormalizing the response values and corresponding scales, to thendetermine modified or normalized distances and the correspondingmodified or normalized degrees or percentages of alignment.

There are many modification or normalization methodologies andstatistical measures available, such as quantile normalization, cosinesimilarity (e.g., when the responses are represented as data vectors),feature scaling, etc., for example and without limitation. For example,a difference vector formed by subtracting the co-respondent D'sresponses from the respondent Y's responses would result in a uniformdifference vector of [1, 1, 1] for the example responses illustrated inFIG. 10 , indicating a high degree of alignment, while a differencevector formed by subtracting the co-respondent D's responses from therespondent Z's responses would result in a nonuniform difference vectorof [4, −3, 4] for the example responses illustrated in FIG. 10 ,indicating a low degree of alignment. In a representative embodiment,the scoring generator 235 normalizes the distances and degrees ofalignment by determining how many “points” were available for respondingto the return queries 615, 620, and of those available points, how manypoints did the respondent or co-respondent actually utilize inresponding to the return queries 615, 620. The distance and alignmentcalculations are then performed again, but using the modified ornormalized response values and a correspondingly modified or normalized(expanded) scale, to generate modified or normalized alignment scoresfor each response to the return queries 615, 620.

Continuing with the examples of FIG. 10 , and assuming only threeresponses to return queries 615, 620 (instead of the more typical highernumber, such as 50), the total number of points available for respondingto the return queries 615, 620 is equal to fifteen (as an absolutevalue, a maximum of 5 points in each direction, for 3 questions), and ofthese, the co-respondent D distributed 7, and the first respondent Ydistributed 6. In normalizing the responses, each response value ismultiplied by the total number available to distribute divided by thenumber actually distributed, for each respondent and co-respondentindividually; using the examples of FIG. 10 , co-respondent D'sresponses will be multiplied by 15/7, and respondent Y's responses willbe multiplied by 15/6. Using a vector notation, co-respondent D'sresponses of [−3, 2, −2] are now normalized to [−6.43, 4.29, −4.29], andrespondent Y's responses of [−2, 3, −1] are now normalized to [5, 7.5,2.5]. The distance and alignment calculations are then performed again,but using the normalized response values and a correspondinglynormalized (expanded) scale, e.g., the scale of −5 to +5 is now expandedto a scale of −15 to +15 (as the cumulative total of the absolute valueof endpoints, here, 5+5+5).

As an example of one of the modified or normalized distancecalculations, using the examples of FIG. 10 , for the DY combination ofthe co-respondent and first respondent, for the question “F” 970, thenormalized distance between the responses is equal to 1.43. From theperspective of co-respondent D, the worst response would have been a+15, a distance of 21.43, so the first respondent Y only differs by1.43/21.43, generating a degree or percentage of alignment of 93.3%(=1−(1.43/21.43)), for question “F” 970. From the perspective ofrespondent Y, the worst response also would have been a +15, a distanceof 20, so the co-respondent D only differs by 1.43/20, generating adegree or percentage of alignment of 92.9% (=1−(1.43/20)), for question“F” 970. Comparing these to the non-normalized (unmodified or raw)alignment scores now shows the higher degree of alignment, as expectedfor this combination (for the responses to this return query).

Next, the scoring generator 235 determines the total modified ornormalized degree of alignment for the selected respondent andco-respondent combination, step 535, which may be, for example, the sumor weighted sum of the individual normalized alignment percentages forall of the responses to the return queries 615, 620, or an average(e.g., mean) value of the individual normalized alignment percentagesfor all of the responses to the return queries 615, 620. In arepresentative embodiment, as an option, the scoring generator 235 thenselects a differential weighting for each of the total normalized degreeof alignment and the raw or unmodified degree of alignment, step 540.Also in a representative embodiment, for example, the amount ofweighting of the total normalized degree of alignment is increased whenthe raw or unmodified degree of alignment is higher. For example andwithout limitation, in a representative embodiment: (1) if the raw orunmodified degree of alignment is under 25%, the total modified ornormalized degree of alignment is given a zero weighting, so that onlythe raw or unmodified degree of alignment is utilized in the finalalignment score; (2) if the raw or unmodified degree of alignment isbetween 25% to 50%, the raw or unmodified degree of alignment is giventhree times the weighting compared to the total normalized degree ofalignment (3:1 or ¾ to ¼ weightings) in generating the final alignmentscore; and (3) if the raw or unmodified degree of alignment is betweenover 50%, the raw or unmodified degree of alignment is weighted equallywith the total modified or normalized degree of alignment (1:1) ingenerating the final alignment score. Using the differential weighting,if any, the scoring generator 235 combines (e.g., sums) the weighted rawor unmodified degree of alignment and the weighted total normalizeddegree of alignment, step 545, to produce the pair-wise alignment scorefor that selected respondent and co-respondent combination. When thereare additional respondent and co-respondent combinations, step 550, thescoring generator 235 iterates, returning to step 505 to select the nextcombination for alignment scoring, and otherwise the method may end,return step 555.

FIG. 11 illustrates respondent and co-respondent customized digitalfilters, represented both as matrices (e.g., 350, 360, 370) in FIGS.11A, 11B) and vectors (e.g., 350 _(A), 360 _(A), 370 _(A)) in FIG. 11C),and with corresponding difference vectors (FIGS. 11D and 11E). FIG. 12is a flow diagram illustrating an exemplary or representative methodembodiment for pair-wise alignment score determination using customizeddigital filters for customized filtering and/or personalization ofsearch results and search result ranking in a search engine. Thesecustomized digital filters provide two-stage or bilateral filtering forgeneration of highly personalized search results and search resultrankings, with personalized search results and search result rankingsreturned to the user for information (e.g., the respondent andco-respondent as represented by the respondent and co-respondentcustomized digital filters (350, 360, 370, 350 _(A), 360 _(A), 370 _(A))which meets the requirements, within a predetermined variance, of bothrespondent and co-respondent customized digital filters (350, 360, 370,350 _(A), 360 _(A), 370 _(A)), i.e., the information “passes through”both the respondent and co-respondent customized digital filters (350,360, 370, 350 _(A), 360 _(A), 370 _(A)), as respective first and secondfilter stages.

In addition, using the respondent and co-respondent customized digitalfilters (350, 360, 370, 350 _(A), 360 _(A), 370 _(A)) provides for anincreased speed of searching, as any selected respondent andco-respondent combination or pair of customized digital filters (350,360, 370, 350 _(A), 360 _(A), 370 _(A)) may be rapidly overlaid orcompared with each other, and when the respondent and co-respondentcombination of customized digital filters are aligned or match within apredetermined variance, the information is included in the personalizedsearch results and search result rankings for the respondent andco-respondent. Using the respondent and co-respondent customized digitalfilters (350, 360, 370, 350 _(A), 360 _(A), 370 _(A)) also decreases theamount of data needed to be stored in memory 205 or in a database 220,and further serves to simplify the database structure, which can berepresented, for example, by a column number representing a selectedreturn query 615, 620 and a response value, as illustrated.

Each of the respondent and co-respondent customized digital filters(350, 360, 370, 350 _(A), 360 _(A), 370 _(A)) are populated, as a matrix(e.g., 350, 360, 370) or as a vector (e.g., 350 _(A), 360 _(A), 370_(A)), for example, with the user's (respondent or co-respondent)responses to the return queries 615, 620, which may be raw (unmodified)or modified (e.g., normalized) data. As represented by the matrices(e.g., 350, 360, 370), the user responses to the return queries 615, 620are represented positionally in the customized digital filters, with thecolumn indicating a selected return query 615, 620, and a “1” in a rowindicating the response to the selected return query 615, 620, such asthe first column of matrix 350 indicating a response of “4” out of 5possible responses, the second column of matrix 350 indicating aresponse of “1” out of 5 possible responses, the third column of matrix350 indicating a response of “2” out of 5 possible responses, and so on.The user's (respondent or co-respondent) responses to the return queries615, 620 are represented equivalently by the customized digital filterillustrated in a vector form as vector 350 _(A), with each rowrepresenting the response to a return query 615, 620.

Referring to FIG. 12 , as part of step 430, the scoring generator 235starts, step 1000, by selecting a relevant respondent and co-respondentcombination for determination of the pair-wise alignment score for thatcombination, step 1005. Next, in step 1010, the scoring generator 235compares the respondent and co-respondent customized digital filters350, 350 _(A) (or 370, 370 _(A)) and 360, 360 _(A), and in step 1015,determines the degree of alignment of the selected digital filters andgenerates a corresponding pair-wise score, as discussed above. As anadditional embodiment, the degree of alignment may be based ondetermination of a variance between the digital filters, and if thevariance is within a predetermined amount, level or threshold, step1020, search results are returned which include the respondent andco-respondent combination, step 1025, and otherwise not returned to theuser. Following step 1020 or 1025, the method may end, return step 1030.

Comparing the respondent and co-respondent customized digital filters350, 350 _(A) and 360, 360 _(A), it can be rapidly determined that theyare highly aligned, differing only by one value in the respective lastcolumns (a “5” versus a “4”) or last row of vectors 350 _(A), 360 _(A).This comparison may be accomplished quite rapidly, such as bydetermination of a difference matrix or vector, which in this case wouldshow a difference of only a value of one for one response to a returnquery 615, 620, as illustrated in difference vector 380 _(A) of FIG.11D.

The rapid comparison of the respondent and co-respondent customizeddigital filters 350 and 360 indicates a high degree of alignment, anddepending upon the amount of predetermined variance selected by theuser, would indicate that the information represented by the customizeddigital filters (350, 360, 350 _(A), 360 _(A)) should be provided in thepersonalized search results and search result rankings, such as anemployment opportunity conveyed to an employment candidate and thecandidate information conveyed to a potential employer, respectively, asthe respondent and co-respondent pair or combination.

Similarly, comparing the respondent and co-respondent customized digitalfilters 370 and 360, it can be rapidly determined that they are alsohighly aligned when normalized or modified, as each response to a returnquery 615, 620 of customized digital filter 370 is shifted only by onevalue, and when shifted, exhibit complete alignment. This comparisonalso may be accomplished quite rapidly, also such as by determination ofa difference matrix or vector, which in this case would show aconsistent difference of a value of one for all responses to returnqueries 615, 620, as illustrated in difference vector 390 _(A) of FIG.11E. The rapid comparison of the respondent and co-respondent customizeddigital filters 370 and 360 indicates a high degree of alignment, anddepending upon the amount of predetermined variance selected by theuser, would also indicate that the information represented by thecustomized digital filters (370, 360, 370 _(A), 360 _(A)) should beprovided in the personalized search results and search result rankings,such as an employment opportunity conveyed to an employment candidateand the candidate information conveyed to a potential employer,respectively, as the respondent and co-respondent pair or combination.

It should be noted that this two-stage filtering is highly new andnovel. One (first) stage of the filtering is user-customizable, basedupon the user's (respondent or co-respondent) responses to the returnqueries 615, 620. The second stage of filtering, however, iscustomizable by a third party, namely, another respondent orco-respondent whose information may or may not be returned to the userin the user's personalized network search results and search resultrankings, depending upon the alignment between the user's digital filterand the third party's digital filter.

In a representative embodiment, the one or more processors may befurther adapted to use the plurality of digital filters (350, 360, 370,350 _(A), 360 _(A), 370 _(A)) to provide a two-stage filtering ofpotential search results through both a respondent digital filter of aselected respondent and a co-respondent digital filter of a selectedco-respondent, of the plurality of digital filters (350, 360, 370, 350_(A), 360 _(A), 370 _(A)), to generate the personalized network searchresults and search result rankings for the selected respondent or theselected co-respondent. For example, a first co-respondent may beincluded in the personalized network search results and search resultrankings returned to the selected respondent when the informationpertaining to the first co-respondent meets the requirements, within apredetermined variance or difference, of both the co-respondent digitalfilter of the first co-respondent, as a first stage of filtering, andthe respondent digital filter of the selected respondent, as a secondstage of filtering. Similarly, also for example, a first respondent maybe included in the personalized network search results and search resultrankings returned to the selected co-respondent when the informationpertaining to the first respondent meets the requirements, within apredetermined variance or difference, of both the respondent digitalfilter of the first respondent, as a first stage of filtering, and theco-respondent digital filter of the selected co-respondent, as a secondstage of filtering.

In summary, the representative embodiments provide a technical,artificial intelligence solution to an Internet-centric problem ofover-inclusiveness and under-inclusiveness of search results anddistorted rankings of search results using the prior art keywordsearching. The representative embodiments automate the Internet-basedsearching and selection processes using highly relevant, user-determinedcharacteristics and user-customizable parameters, resulting inpersonalization of search results and search result ranking. Therepresentative embodiments employ artificial intelligence to “match”information to a user (as a respondent or co-respondent) and providewith greater precision the information the user wants or needs (ifavailable) at the point in time wanted or needed, without inundating theuser with thousands of responses or documents which the user cannotpossibly review in a reasonable or allocated time.

As a result, the representative embodiments improve the functioning ofInternet-based searches, providing highly personalized search resultsand search result rankings, thereby dramatically decreasing the amountof search time required for a user to discover relevant and actionableinformation.

The representative embodiments have particular advantages in variouscontexts, such as employment searching. The representative embodimentsenable personalization of search results and search result rankings, forboth an employment candidate and a potential employer, with dramatictime savings and with much more highly relevant results. Therepresentative embodiments also enable “blind” searching, without regardto race, religion, gender, ethnicity (which may be apparent in acandidate's name, for example), age, and so on, which is highly relevantfor legal compliance and documentation. The personalized search resultsand search result rankings may also be useful in other ways, such asindicating to an employer other avenues for recruiting, such as postingson additional sites that also can feed into the server system 200.Dashboards may also be created for various contexts, such as foruniversities, who may then see how their graduates are doing in the jobmarket.

It should be noted that the representative embodiments provide atechnical solution to a technical, Internet-centric problem ofover-inclusiveness of search results and distorted rankings of searchresults using the prior art keyword searching. For example, in theemployment context, this problem did not exist in the pre-Internet age,and a company would not receive hundreds of thousands of candidateresumes each year. Rather, a company having an employment opening orlooking to hire potential candidates typically provided physical, printadvertisements in local newspapers, technical and professional journals,or hosted or participated in multi-employer job fairs, for example, witha potential candidate seeing the advertisement or attending the job fairand applying for the position, resulting in the company receiving 10-100candidate resumes in response, also for example, which could all bereviewed in a comparatively short period of time.

Similarly, the representative embodiments are also necessarily technicaland improve the functioning of the Internet and networked computingdevices as a whole, providing an artificial intelligence solution tothis Internet-centric problem by enabling personalization of searchresults and search result ranking.

Lastly, it should also be noted that the representative embodimentscannot merely be performed by hand or in the human mind. As mentionedabove, using conservative estimates, it would not be unusual for 100,000candidates worldwide to be seeking new employment opportunities and foremployers to be seeking to fill 100,000 employment opportunitiesworldwide (and in the aggregate). To score 100,000 candidates acrosspotentially 100,000 employment opportunities would result in 10 billionpossible combinations, with scoring across responses to 50 returnqueries 615, 620 resulting in 500 billion comparisons. Assuming a highlycapable person could perform each comparison in 30 seconds (2/minute),would then take 250 billion minutes, translating to over 4.167 billionhours, and for an eight hour work day per person, consume 520,833,333person-days, or stated more simply, would take 1,426,940 person-years,which are clearly not available when trying to fill a position in thenext month or two! Even much more conservative estimates of the numberof candidates to be scored for employment opportunities, such as 10,000candidates across potentially 10,000 employment opportunities wouldresult in 100 million possible combinations, with scoring acrossresponses to 50 return queries 615, 620 resulting in 5 billioncomparisons. Again assuming a highly capable person who could performeach comparison in 30 seconds (2/minute), would then take 2.5 billionminutes, translating to over 41 million hours, and for an eight hourwork day per person, consume 5,208,333 person-days, or stated moresimply, would take 14,269 person-years, which is an equallyinsurmountable task for time-sensitive information and requirements.

As used herein, a processor 210, 305 (and, for the processor 210, itsincorporated return query generator 230, scoring generator 235, rankinggenerator 240, and rank modification generator 245) may be implementedusing any type of digital or analog electronic or other circuitry whichis arranged, configured, designed, programmed or otherwise adapted toperform any portion of the personalization of search results and searchresult rankings functionality, described herein. As the term processoris used herein, a processor 210, 305 may include use of a singleintegrated circuit (“IC”), or may include use of a plurality ofintegrated circuits or other electronic components connected, arrangedor grouped together, such as processors, controllers, microprocessors,digital signal processors (“DSPs”), parallel processors, multiple coreprocessors, custom ICs, application specific integrated circuits(“ASICs”), field programmable gate arrays (“FPGAs”), adaptive computingICs, discrete electronic components, and any associated memory (such asRAM, DRAM and ROM), and other ICs and components, whether analog ordigital. As a consequence, as used herein, the term processor should beunderstood to equivalently mean and include a single IC, or arrangementof custom ICs, ASICs, processors, microprocessors, controllers, FPGAs,adaptive computing ICs, or some other grouping of integrated circuits ordiscrete electronic components which perform the functions discussedabove and further discussed below, and may further include anyassociated memory, such as microprocessor memory or additional RAM,DRAM, SDRAM, SRAM, MRAM, ROM, FLASH, EPROM or EPROM. A processor (suchas processor 210, 305), with any associated memory, may be arranged,adapted or configured (via programming, FPGA interconnection, orhard-wiring) to perform any portion of the personalization of searchresults and search result rankings of the present disclosure, asdescribed herein. For example, the methodology may be programmed andstored, in a processor 210, 305 with its associated memory (and/ormemory 205, 310, respectively) and other equivalent components, as a setof program instructions or other code (or equivalent configuration orother program) for subsequent execution when the processor 210, 305 isoperative (i.e., powered on and functioning). Equivalently, when theprocessor 210, 305 may implemented in whole or part as FPGAs, custom ICsand/or ASICs, the FPGAs, custom ICs or ASICs also may be designed,configured and/or hard-wired to implement any portion of thepersonalization of search results and search result rankings of thepresent disclosure. For example, the processor 210, 305 may beimplemented as an arrangement of analog and/or digital circuits,controllers, microprocessors, DSPs and/or ASICs, collectively referredto as a “processor”, which are respectively hard-wired, arranged,programmed, designed, adapted or configured to implement personalizationof search results and search result rankings of the present disclosure,including possibly in conjunction with a memory 205, 310.

The operations described in this disclosure can be implemented asoperations performed by a data processing apparatus on data stored onone or more machine-readable storage devices or on data received fromother sources. Server system 200 is an example of a representative dataprocessing apparatus. As utilized herein, the terminology “dataprocessing apparatus” encompasses any and all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor (210, 305), a computer, a server, a system on achip (“SOC”), or combinations of such devices. The server system 200 canalso include code (such as executable code) that creates an executionenvironment for a data processing apparatus, or other program, such asprocessor 210 firmware, a protocol stack, a database (220, 220A)management system, an operating system, a cross-platform runtimeenvironment, a virtual machine, and/or combinations thereof, which maybe utilized in a computer, a server 200, or other data processingapparatus.

A memory 205, 310 and/or a data storage device 250 may be embodied asany type of data storage device, such as RAM, FLASH, DRAM, SDRAM, SRAM,MRAM, FeRAM, ROM, EPROM or E²PROM, and is utilized for data storage, andalso may be utilized to store any program instructions or configurationswhich may be utilized by a processor 210, 305. More specifically, thememory 205, 310 and/or a data storage device 250 may be embodied in anynumber of forms, including within any nontransitory, machine-readabledata storage medium, memory device or other storage or communicationdevice for storage or communication of information, currently known orwhich becomes available in the future, including, but not limited to, amemory integrated circuit (“IC”), or memory portion of an integratedcircuit (such as the resident memory within a processor 210, 305 orprocessor IC), whether volatile or non-volatile, whether removable ornon-removable, including without limitation RAM, FLASH, DRAM, SDRAM,SRAM, MRAM, FeRAM, ROM, EPROM or E²PROM, or any other form of memory ordata storage device, such as a magnetic hard drive, an optical drive, amagnetic disk or tape drive, a hard disk drive, other machine-readablestorage or memory media such as a floppy disk, a CDROM, a CD-RW, digitalversatile disk (DVD) or other optical memory, or any other type ofmemory, storage medium, or data storage apparatus or circuit, which isknown or which becomes known, depending upon the selected embodiment.The memory 205, 310 and/or a data storage device 250 may store data inany way or configuration, including as various look up tables,parameters, coefficients, databases, other information and data,programs or instructions (of the software of the present invention), andother types of tables such as database tables or any other form of datarepository.

The network interface (I/O) circuits 215, 315 may be implemented asknown or may become known in the art, and may include impedance matchingcapability, voltage rectification circuitry, voltage translation for alow voltage processor to interface with a higher voltage control bus forexample, various switching mechanisms (e.g., transistors) to turnvarious lines or connectors on or off in response to signaling from aprocessor 210, 305, other control logic circuitry, and/or physicalcoupling mechanisms. In addition, the network interface (I/O) circuits215, 315 are also adapted to receive and/or transmit signals externallyto the server system 200 and client device 300, 300A, respectively, suchas through hard-wiring or RF signaling, for example, to receive andtransmit information in real-time, such as queries 605, 610, returnqueries 615, 620, and personalized search results and search resultrankings, also for example. The network interface (I/O) circuits 215,315 also may be stand-alone devices (e.g., modular). The networkinterface (I/O) circuits 215, 315 are utilized for appropriateconnection to a relevant channel, network or bus; for example, thenetwork interface (I/O) circuits 215, 315 may provide impedancematching, drivers and other functions for a wireline interface, mayprovide demodulation and analog to digital conversion for a wirelessinterface, and may provide a physical interface for the memory 205, 310with other devices. In general, the network interface (I/O) circuits215, 315 are used to receive and transmit data, depending upon theselected embodiment, such as queries 605, 610, return queries 615, 620,and personalized search results and search result rankings, controlmessages, authentication data, profile information, and other pertinentinformation.

As indicated above, the processor 210, 305 is hard-wired, configured orprogrammed, using software and data structures of the invention, forexample, to perform any portion of the automated personalization ofsearch results and search result rankings, of the present disclosure. Asa consequence, the system and method of the present disclosure may beembodied as software which provides such programming or otherinstructions, such as a set of instructions and/or metadata embodiedwithin a nontransitory computer-readable medium, discussed above. Inaddition, metadata may also be utilized to define the various datastructures of a look up table or a database. Such software may be in theform of source or object code, by way of example and without limitation.Source code further may be compiled into some form of instructions orobject code (including assembly language instructions or configurationinformation). The software, source code or metadata of the presentinvention may be embodied as any type of code, such as C, C++,Javascript, Adobe Flash, Silverlight, SystemC, LISA, XML, Java, Brew,SQL and its variations (e.g., SQL 99 or proprietary versions of SQL),DB2, Oracle, or any other type of programming language which performsthe functionality discussed herein, including various hardwaredefinition or hardware modeling languages (e.g., Verilog, VHDL, RTL) andresulting database files (e.g., GDSII). As a consequence, “software”,“program”, “computer program”, or a “module”, “program module”,“software module”, as used equivalently herein, means and refers to anyprogramming language, of any kind, with any syntax or signatures, whichprovides or can be interpreted to provide the associated functionalityor methodology specified (when instantiated or loaded into a processoror computer and executed, including the processor 210, 305, forexample). In addition, any of such program or software modules may becombined or divided in any way. For example, a larger module combiningfirst and second functions is considered equivalent to a first modulewhich performs the first function and a separate second module whichperforms the second function.

For example, a computer program (e.g., a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled, interpreted, declarative, or procedurallanguages. Such a program may be implemented in any form, including as astand-alone program or as a module, component, subroutine, object, orother construct which may be used in a computing environment, and may bestored as a file, a file system, multiple files, or portion of a filewhich includes other programs or data, such as a script stored in amarkup language document, and may be executed on one or more computers,servers 200, or other data processing apparatus that are co-located ordistributed across multiple locations and interconnected by a networksuch as network or internet 110.

The software, metadata, or other source code of the present inventionand any resulting bit file (object code, database, or look up table) maybe embodied within any tangible, non-transitory storage medium, such asany of the computer or other machine-readable data storage media, ascomputer-readable instructions, data structures, program modules orother data, such as discussed above with respect to the memory 205, 310,e.g., a memory IC, a floppy disk, a CDROM, a CD-RW, a DVD, a magnetichard drive, an optical drive, or any other type of data storageapparatus or medium, as mentioned above.

In addition to the server system 200 illustrated in FIG. 2 , thosehaving skill in the art will recognize that there are innumerableequivalent configurations, layouts, kinds and types of server andcontrol circuitry known in the art, which are within the scope of thepresent invention.

The present disclosure is to be considered as an exemplification of theprinciples of the invention and is not intended to limit the inventionto the specific embodiments illustrated. In this respect, it is to beunderstood that the invention is not limited in its application to thedetails of construction and to the arrangements of components set forthabove and below, illustrated in the drawings, or as described in theexamples. Systems, methods and apparatuses consistent with the presentinvention are capable of other embodiments and of being practiced andcarried out in various ways.

Although the invention has been described with respect to specificembodiments thereof, these embodiments are merely illustrative and notrestrictive of the invention. In the description herein, numerousspecific details are provided, such as examples of electroniccomponents, electronic and structural connections, materials, andstructural variations, to provide a thorough understanding ofembodiments of the present invention. One skilled in the relevant artwill recognize, however, that an embodiment of the invention can bepracticed without one or more of the specific details, or with otherapparatus, systems, assemblies, components, materials, parts, etc. Inother instances, well-known structures, materials, or operations are notspecifically shown or described in detail to avoid obscuring aspects ofembodiments of the present invention. One having skill in the art willfurther recognize that additional or equivalent method steps may beutilized, or may be combined with other steps, or may be performed indifferent orders, any and all of which are within the scope of theclaimed invention. In addition, the various Figures are not drawn toscale and should not be regarded as limiting.

Reference throughout this specification to “one embodiment”, “anembodiment”, or a specific “embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present invention and notnecessarily in all embodiments, and further, are not necessarilyreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics of any specific embodiment of the presentinvention may be combined in any suitable manner and in any suitablecombination with one or more other embodiments, including the use ofselected features without corresponding use of other features. Inaddition, many modifications may be made to adapt a particularapplication, situation or material to the essential scope and spirit ofthe present invention. It is to be understood that other variations andmodifications of the embodiments of the present invention described andillustrated herein are possible in light of the teachings herein and areto be considered part of the spirit and scope of the present invention.

It will also be appreciated that one or more of the elements depicted inthe Figures can also be implemented in a more separate or integratedmanner, or even removed or rendered inoperable in certain cases, as maybe useful in accordance with a particular application. Integrally formedcombinations of components are also within the scope of the invention,particularly for embodiments in which a separation or combination ofdiscrete components is unclear or indiscernible. In addition, use of theterm “coupled” herein, including in its various forms such as “coupling”or “couplable”, means and includes any direct or indirect electrical,structural or magnetic coupling, connection or attachment, or adaptationor capability for such a direct or indirect electrical, structural ormagnetic coupling, connection or attachment, including integrally formedcomponents and components which are coupled via or through anothercomponent.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue. For example, a dimension disclosed as “40 mm” is intended to mean“about 40 mm.”

All documents cited in the Detailed Description of the Invention are, inrelevant part, incorporated herein by reference; the citation of anydocument is not to be construed as an admission that it is prior artwith respect to the present invention. To the extent that any meaning ordefinition of a term in this document conflicts with any meaning ordefinition of the same term in a document incorporated by reference, themeaning or definition assigned to that term in this document shallgovern.

Furthermore, any signal arrows in the drawings/Figures should beconsidered only exemplary, and not limiting, unless otherwisespecifically noted. Combinations of components of steps will also beconsidered within the scope of the present invention, particularly wherethe ability to separate or combine is unclear or foreseeable. Thedisjunctive term “or”, as used herein and throughout the claims thatfollow, is generally intended to mean “and/or”, having both conjunctiveand disjunctive meanings (and is not confined to an “exclusive or”meaning), unless otherwise indicated. As used in the description hereinand throughout the claims that follow, “a”, “an”, and “the” includeplural references unless the context clearly dictates otherwise. Also asused in the description herein and throughout the claims that follow,the meaning of “in” includes “in” and “on” unless the context clearlydictates otherwise.

The foregoing description of illustrated embodiments of the presentinvention, including what is described in the summary or in theabstract, is not intended to be exhaustive or to limit the invention tothe precise forms disclosed herein. From the foregoing, it will beobserved that numerous variations, modifications and substitutions areintended and may be effected without departing from the spirit and scopeof the novel concept of the invention. It is to be understood that nolimitation with respect to the specific methods and apparatusillustrated herein is intended or should be inferred. It is, of course,intended to cover by the appended claims all such modifications as fallwithin the scope of the claims.

It is claimed:
 1. A computation system coupleable to a network forpersonalization of employment search results and search result rankings,the computation system comprising: a network input and output interfaceconfigured to transmit and receive network data, the network input andoutput interface further configured to receive at least one query from arespondent or a co-respondent via the network, the at least one querypertaining to employment; to transmit a plurality of return queries tothe respondent or co-respondent via the network; to receive a pluralityof responses to the plurality of return queries from the respondent orco-respondent via the network; and to transmit personalized employmentsearch results and search result rankings to the respondent orco-respondent via the network; at least one data storage deviceconfigured to store the plurality of return queries; and one or moreprocessors coupled to the at least one data storage device and to thenetwork input and output interface, the one or more processorsconfigured to access the at least one data storage device and using theat least one query, to select the plurality of return queries pertainingto employment for transmission; to search the at least one data storagedevice for corresponding pluralities of responses to the plurality ofreturn queries from one or more co-respondents or respondents,respectively; to comparatively pair-wise score the plurality ofresponses to the plurality of return queries against the correspondingpluralities of responses to the plurality of return queries and generatea plurality of pair-wise alignment scores for a plurality of respondentand co-respondent combinations; to sort and rank the plurality ofrespondent and co-respondent combinations according to the plurality ofpair-wise alignment scores; and the one or more processors furtherconfigured to use the sorted and ranked plurality of respondents orco-respondents combinations to generate and output the personalizedemployment search results and search result rankings, the personalizedemployment search results and search result rankings comprising one ormore identifications of sorted and ranked respondents or co-respondentsfor the co-respondent or respondent, respectively.
 2. The computationsystem of claim 1, wherein the one or more processors are furtherconfigured to select one or more co-respondents or respondents from thesorted and ranked plurality of respondent and co-respondent combinationsfor inclusion of a predetermined number of sorted and ranked respondentsor co-respondents in the personalized employment search results andsearch result rankings.
 3. The computation system of claim 1, whereinthe one or more processors are further configured, for each response ofthe plurality of responses to the plurality of return queries, todetermine an unmodified distance between responses of a respondent and aco-respondent; and to combine a plurality of unmodified distancedeterminations for the plurality of responses to the plurality of returnqueries to form an unmodified alignment score.
 4. The computation systemof claim 3, wherein the one or more processors are further configured,for each response of the plurality of responses to the plurality ofreturn queries, to determine a normalized distance between each responseof a respondent and a co-respondent; and to combine a plurality ofnormalized distance determinations for the plurality of responses to theplurality of return queries to form a normalized alignment score.
 5. Thecomputation system of claim 4, wherein the one or more processors arefurther configured to differentially weight the unmodified alignmentscore and normalized alignment score; and to combine the differentiallyweighted unmodified alignment score and normalized alignment score toform a pair-wise alignment score of the plurality of pair-wise alignmentscores.
 6. The computation system of claim 1, wherein the one or moreprocessors are further configured to generate a digital filter from theplurality of responses to the plurality of return queries to form aplurality of digital filters, wherein each digital filter of theplurality of digital filters comprises a matrix or vector having theplurality of responses to the plurality of return queries for a selectedrespondent or co-respondent.
 7. The computation system of claim 6,wherein the one or more processors are further configured to compare,using a variance determination or a difference determination, a selectedcombination of respondent and co-respondent digital filters, of theplurality of digital filters, to generate a pair-wise alignment score,of the plurality of pair-wise alignment scores, for the selectedrespondent and co-respondent combination.
 8. The computation system ofclaim 6, wherein the one or more processors are further configured touse the plurality of digital filters to provide a two-stage filtering ofpotential search results through both a respondent digital filter of aselected respondent and a co-respondent digital filter of a selectedco-respondent, of the plurality of digital filters, to generate thepersonalized employment search results and search result rankings forthe selected respondent or the selected co-respondent.
 9. Thecomputation system of claim 1, wherein the one or more processors arefurther configured to store the plurality of pair-wise alignment scoresfor the plurality of respondent and co-respondent combinations in the atleast one data storage device, and wherein the one or more processorsare further configured to store the one or more identifications of thesorted and ranked respondents or co-respondents in the at least one datastorage device.
 10. The computation system of claim 1, wherein at leastone return query of the plurality of return queries to the respondent orco-respondent pertains to a commute time or pertains to access totransportation.
 11. The computation system of claim 1, wherein the oneor more processors are further configured to filter the one or moreidentifications of the sorted and ranked respondents or co-respondentsusing a user-selectable parameter, of a plurality of user-selectableparameters, selected from the group consisting of: a previous employer,a current employer, a previous employee, a current employee,citizenship, a disability status, a visa status, military service, acommute time, access to transportation, a geographic location, andcombinations thereof.
 12. The computation system of claim 1, wherein theone or more identifications of the sorted and ranked respondents orco-respondents comprises one or more identifications of sorted andranked employment candidates provided to a potential employer orcomprises one or more identifications of sorted and ranked potentialemployers provided to an employment candidate.
 13. The computationsystem of claim 1, wherein each return query a first plurality of returnqueries to the respondent is a corollary to each return query of asecond plurality of return queries to the co-respondent.
 14. Thecomputation system of claim 1, wherein each return query of theplurality of return queries to a respondent pertains to a preference orinterest level of one or more characteristics of the at least one query.15. A computer system-implemented method for network search andpersonalization of employment search results and search result rankings,the computer system comprising a network input and output interface, atleast one data storage device, and one or more processors coupled to theat least one data storage device and to the network input and outputinterface, the method comprising: using the network input and outputinterface, receiving at least one query from an employment candidate asrespondent or a potential employer as a co-respondent via the network,the at least one query pertaining to employment; in response to the atleast one query, using the one or more processors, accessing the atleast one data storage device and selecting a plurality of returnqueries pertaining to one or more characteristics or features of anemployment position, and at least one return query of the plurality ofreturn queries pertaining to a commute time or access to transportation;using the network input and output interface, transmitting the pluralityof return queries to the respondent or co-respondent via the network;using the network input and output interface, receiving a plurality ofresponses to the return queries from the respondent or co-respondent viathe network; using the one or more processors, searching the at leastone data storage device for corresponding pluralities of responses tothe plurality of return queries from one or more co-respondents orrespondents, respectively; using the one or more processors,comparatively pair-wise scoring the plurality of responses to theplurality of return queries against the corresponding pluralities ofresponses to the plurality of return queries and generating a pluralityof pair-wise alignment scores for a plurality of respondent andco-respondent combinations; using the one or more processors, sortingand ranking the plurality of respondent and co-respondent combinationsaccording to the plurality of pair-wise alignment scores; using the oneor more processors, from the sorted and ranked plurality of respondentand co-respondent combinations, generating the personalized employmentsearch results and search result rankings, the personalized employmentsearch results and search result rankings comprising one or moreidentifications of sorted and ranked respondents or co-respondents forthe co-respondent or respondent, respectively; and using the networkinput and output interface, transmitting the personalized employmentsearch results and search result rankings.
 16. The computersystem-implemented method of claim 15, further comprising: using the oneor more processors, selecting one or more co-respondents or respondentsfrom the sorted and ranked plurality of respondent and co-respondentcombinations for inclusion of a predetermined number of sorted andranked respondents or co-respondents in the personalized network searchresults and search result rankings.
 17. The computer system-implementedmethod of claim 15, wherein the pair-wise scoring further comprises: foreach response of the plurality of responses to the plurality of returnqueries, using the one or more processors, determining an unmodifieddistance between each response of a respondent and a co-respondent; andusing the one or more processors, combining a plurality of unmodifieddistance determinations for the plurality of responses to the pluralityof return queries to form an unmodified alignment score; for eachresponse of the plurality of responses to the plurality of returnqueries, using the one or more processors, determining a normalizeddistance between each response of a respondent and a co-respondent; andusing the one or more processors, combining a plurality of normalizeddistance determinations for the plurality of responses to the pluralityof return queries to form a normalized alignment score; using the one ormore processors, differentially weighting the unmodified alignment scoreand normalized alignment score; and using the one or more processors,combining the differentially weighted unmodified alignment score andnormalized alignment score to form each pair-wise alignment score of theplurality of pair-wise alignment scores.
 18. The computersystem-implemented method of claim 15, further comprising: using the oneor more processors, generating a digital filter from the e plurality ofresponses to the plurality of return queries to form a plurality ofdigital filters, wherein each digital filter of the plurality of digitalfilters comprises a matrix or vector having the plurality of responsesto the plurality of return queries for a selected respondent orco-respondent; and using the one or more processors, comparing aselected combination of respondent and co-respondent digital filters, ofthe plurality of digital filters, to generate a pair-wise alignmentscore for the selected respondent and co-respondent combination, of theplurality of pair-wise alignment scores, wherein the comparison is avariance determination or a difference determination.
 19. The computersystem-implemented method of claim 15, further comprising: using the oneor more processors, storing the plurality of pair-wise alignment scoresfor the plurality of respondent and co-respondent combinations in the atleast one data storage device; and using the one or more processors,storing the one or more identifications of the sorted and rankedrespondents or co-respondents in the at least one data storage device.20. The computer system-implemented method of claim 15, furthercomprising: using the one or more processors, filtering the one or moreidentifications of the sorted and ranked respondents or co-respondentsusing a user-selectable parameter, of a plurality of user-selectableparameters, selected from the group consisting of: a previous employer,a current employer, a previous employee, a current employee,citizenship, a disability status, a visa status, military service, acommute time, access to transportation, a geographic location, andcombinations thereof.
 21. The computer system-implemented method ofclaim 15, wherein the one or more identifications of the sorted andranked respondents or co-respondents comprises one or moreidentifications of sorted and ranked employment candidates provided to apotential employer or comprises one or more identifications of sortedand ranked potential employers provided to an employment candidate. 22.A computation system coupleable to a network for personalization ofemployment search results and search result rankings, the computationsystem comprising: a network input and output interface configured totransmit and receive network data, the network input and outputinterface further configured to receive, via the network, at least onequery from an employment candidate as a respondent or a potentialemployer as a co-respondent; to transmit a plurality of return queriesto the respondent or co-respondent via the network; to receive aplurality of responses to the plurality of return queries from therespondent or co-respondent via the network; and to transmitpersonalized employment search results and search result rankings to therespondent or co-respondent via the network; at least one data storagedevice configured to store the plurality of return queries; and one ormore processors coupled to the at least one data storage device and tothe network input and output interface, the one or more processorsconfigured to access the at least one data storage device and using theat least one query, to select the plurality of return queries fortransmission, the plurality of return queries pertaining to one or morecharacteristics or features of an employment position, and at least onereturn query of the plurality of return queries pertaining to a commutetime or access to transportation; to search the at least one datastorage device for corresponding pluralities of responses to theplurality of return queries from one or more co-respondents orrespondents, respectively; to comparatively pair-wise score theplurality of responses to the plurality of return queries against thecorresponding pluralities of responses to the return queries usingdifferentially weighted unmodified alignment scores and normalizedalignment scores and generate a plurality of pair-wise alignment scoresfor a plurality of respondent and co-respondent combinations; to sortand rank the plurality of respondent and co-respondent combinationsaccording to the plurality of pair-wise alignment scores; the one ormore processors further configured to use the sorted and rankedplurality of respondents or co-respondents combinations to generate andoutput the personalized employment search results and search resultrankings, the personalized employment search results and search resultrankings comprising one or more identifications of sorted and rankedrespondents or co-respondents for the co-respondent or respondent,respectively.